The Software Engineer role at Elevi Associates is pivotal to the development and maintenance of innovative software solutions that drive the company's mission. You will contribute to projects that shape the future of technology across various sectors, including cloud computing and network engineering. This position is not just about writing code; it involves collaborating with cross-functional teams to create scalable, reliable, and efficient systems that meet complex user needs. At Elevi Associates, the impact of a Software Engineer is profound. You will work on significant products that enhance operational efficiency and improve user experiences. Engaging in challenging projects, you will have opportunities to influence the design and architecture of systems that handle large volumes of data and support critical operations. Expect to face complex problems that require not only technical skills but also strategic thinking and creativity.
Initial Screening
reportedAn initial assessment to evaluate your background and fit for the role.
What to demonstrate
- An initial assessment to evaluate your background and fit for the role
- Depth in Python
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Assessments
reportedPractical evaluations of your technical skills and problem-solving abilities.
What to demonstrate
- Practical evaluations of your technical skills and problem-solving abilities
- Depth in Python
How to prepare
- Answer aloud and timed: Describe how you would optimize a slow-performing application.
- Answer aloud and timed: Can you explain the concept of microservices architecture?
Behavioral Interviews
reportedInterviews focused on your teamwork, communication, and cultural fit within the company.
What to demonstrate
- Interviews focused on your teamwork, communication, and cultural fit within the company
- Depth in Python
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral interviews above and write down what you would ask to confirm before it.
Final Interviews
reportedConcluding discussions with various stakeholders to assess overall fit and capabilities.
What to demonstrate
- Concluding discussions with various stakeholders to assess overall fit and capabilities
- Depth in Python
How to prepare
- Answer aloud and timed: How would you architect a system to handle real-time data processing?
- Answer aloud and timed: Explain how you would ensure high availability in your system design.
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Practice Coding Regularly: Regular coding practice is essential to build confidence and improve problem-solving skills. Use platforms like LeetCode or HackerRank to hone your abilities.
Going into the loop without having done this.
Prepare Your Questions: Be ready to ask insightful questions during interviews. This not only shows your interest in the role but also helps you evaluate if the company is a good fit for you.
Going into the loop without having done this.
Showcase Your Projects: Bring examples of your work to discuss during the interview. Highlight projects that demonstrate your skills and contributions to team success.
Going into the loop without having done this.
Understand the Company Values: Familiarize yourself with Elevi Associates’ values and culture. Aligning your answers with these values can strengthen your candidacy.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to reverse a linked list.
Write a function to reverse a linked list.
Approach
- Assume a singly linked list of nodes with
valandnext, reversed in place, returning the new head. One pass that flips each node'snextpointer to point backward is enough. - Keep three references:
prev(starts asNone),curr(starts at the head) andnxt. Savenxt = curr.nextbefore overwritingcurr.next, or the rest of the list is lost. - Loop while
curris notNone:nxt = curr.next,curr.next = prev,prev = curr,curr = nxt. When the loop ends,previs the new head. - Complexity: O(n) time, O(1) extra space. A recursive version is also O(n) time but uses O(n) stack and hits Python's default recursion limit of 1000 on long lists.
- Edge cases: empty list (return
None), a single node, and two nodes, the smallest case where forgetting to clear the old head'snextleaves a cycle.
Worked solution 10 min
Iterative three-pointer reversal
- Write
from_listandto_listhelpers first so the reversal can be exercised with ordinary Python lists. - Each iteration detaches
currfrom the unreversed remainder and prepends it to the reversed prefix headed byprev. - When
currfalls off the end,prevholds the former tail, which is returned; an empty input never enters the loop and returnsNone.
class ListNode:
def __init__(self, val, next=None):
self.val = val
self.next = next
def reverse_list(head):
"""Reverse a singly linked list in place and return the new head."""
prev = None
curr = head
while curr:
nxt = curr.next # save the rest before cutting the link
curr.next = prev # point this node backward
prev, curr = curr, nxt
return prev # the old tail is the new head
def from_list(values):
head = None
for v in reversed(values):
head = ListNode(v, head)
return head
def to_list(head):
out = []
while head:
out.append(head.val)
head = head.next
return out
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Follow-up
- Can you do it recursively? Reverse
head.nextfirst, then sethead.next.next = headandhead.next = None; O(n) stack space. - How would you reverse only positions m through n? Walk to the node before m, then repeatedly move the following node to the front of the sublist; one pass, O(1) space.
- How would you reverse in groups of k? Reverse each group of k with the same loop, link the previous group's tail to the new group head, and leave a final short group as is.
How would you implement a sorting algorithm? Describe its time complexity.
How would you implement a sorting algorithm? Describe its time complexity.
Approach
- Interpretation: implement one comparison sort from scratch and analyze it. Merge sort is the safest choice: O(n log n) in every case, stable, and easy to prove; mention quicksort as the alternative.
- Divide the list in half recursively until pieces have 0 or 1 elements. Merge two sorted halves with two indices, always taking the smaller head; taking from the left half on ties (
<=) is what keeps the sort stable. - Complexity: there are about log2 n levels and each level merges n elements in total, so T(n) = 2T(n/2) + O(n) = O(n log n) for best, average and worst case. Extra space is O(n) for merge buffers plus O(log n) recursion stack.
- Contrast the alternatives: quicksort averages O(n log n), sorts in place and is cache-friendly, but hits O(n^2) with bad pivots (first element on sorted input; randomize it) or with many equal keys under a Lomuto partition. Heapsort is O(n log n) worst case and in place but not stable.
- Know the limits: comparison sorts need on the order of n log n comparisons in the worst case. Counting sort, O(n + k) for integer keys in a range of size k, and radix sort, O(d(n + b)) for d-digit keys in base b, beat that by exploiting key structure. Timsort (
sorted) is O(n) on sorted input. - Test edge cases: empty and single-element lists, duplicates, already sorted and reverse-sorted input, negative numbers, and records with equal keys to demonstrate stability.
Worked solution 20 min
Stable merge sort
- Base case: a list with 0 or 1 items is already sorted, so return a copy and never mutate the caller's list.
- Split at
mid = len(items) // 2, sort each half recursively, and pass both results to_merge. _mergeadvances indicesiandjthrough the halves, appending the smaller head; once either half is exhausted, the other's remainder is appended in oneextend.- The optional
keyargument makes stability testable: records with equal keys come out in their original order.
def merge_sort(items, key=lambda x: x):
"""Return a new list with items sorted ascending by key; stable."""
if len(items) <= 1:
return list(items)
mid = len(items) // 2
left = merge_sort(items[:mid], key)
right = merge_sort(items[mid:], key)
return _merge(left, right, key)
def _merge(left, right, key):
merged = []
i = j = 0
while i < len(left) and j < len(right):
# <= takes from the left on ties, keeping equal items in input order
if key(left[i]) <= key(right[j]):
merged.append(left[i])
i += 1
else:
merged.append(right[j])
j += 1
merged.extend(left[i:]) # at most one of these two is non-empty
merged.extend(right[j:])
return merged
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Follow-up
- How would you sort data too large for memory? External merge sort: sort chunks that fit in RAM, write them out as runs, then k-way merge the runs with a min-heap.
- Can merge sort run in O(1) extra space? In-place merging exists but is intricate and slower; if constant space is required, heapsort is the practical O(n log n) choice.
- How would you keep quicksort fast on input with many duplicate keys? Use three-way (Dutch national flag) partitioning so keys equal to the pivot are settled in one pass instead of being recursed on.
Solve the two-sum problem and explain your approach.
Solve the two-sum problem and explain your approach.
Approach
- State the contract: given
numsandtarget, return indices[i, j]of two different positions whose values sum totarget, orNoneif none exists. Ask whether the input is sorted and whether indices or values are wanted, since that changes the best approach. - Brute force checks every pair with nested loops: O(n^2) time, O(1) space. State it as the baseline; the faster version trades O(n) memory for a single pass.
- Key insight: for each value
x, the partner it needs istarget - x. A hash map from value to index of elements already seen answers "have I met the partner?" in O(1) average time, so one pass suffices. - Look up the complement before inserting
x. That ordering handles duplicates ([3, 3], target 6 gives[0, 1]) and stops an element pairing with itself ([3], target 6 must not return[0, 0]). Total O(n) time and O(n) space. - If the array is already sorted, two pointers moving inward from both ends give O(n) time and O(1) space. Sorting first costs O(n log n) and loses the original indices unless you sort (value, index) pairs.
Worked solution 10 min
One-pass hash map
- Iterate with
enumerateso every value comes with its position. - Compute
need = target - xand check it againstseen; a hit returns[seen[need], i], earlier index first. - Only after a miss, record
seen[x] = i; if the loop completes with no hit, returnNone.
def two_sum(nums, target):
"""Return [i, j] with i < j and nums[i] + nums[j] == target, else None."""
seen = {} # value -> index of an earlier element
for i, x in enumerate(nums):
need = target - x
if need in seen: # look up before inserting x
return [seen[need], i]
seen[x] = i
return None
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Follow-up
- What if you need every unique pair of values? Sort, run two pointers, and skip over repeated values after each match; O(n log n) overall.
- How does this extend to three-sum? Sort, fix each element in turn, and run two pointers on the rest for its complement: O(n^2) time.
- What if numbers arrive as a stream? Keep a set of values seen so far and check each arrival's complement; memory grows with the number of distinct values.
Write code that finds the longest substring without repeating characters.
Write code that finds the longest substring without repeating characters.
Approach
- Clarify the output: the longest contiguous run whose characters are all distinct (its length or the substring itself).
"abcabcbb"gives"abc";"pwwkew"gives"wke", because"pwke"is a subsequence, not a substring. - Use a sliding window:
leftmarks the window start, and a dictlastmaps each character to the index where it was most recently seen. Advancerightone character at a time. - When
s[right]was last seen at an index at or afterleft, jumpleftto that index plus one. Skipping the>= leftcheck is the classic bug:leftmoves backward on a stale index, and"abba"wrongly returns 3 instead of 2. - After updating
last[s[right]] = right, compare the window lengthright - left + 1with the best so far. Each index is processed once, so O(n) time and O(min(n, alphabet size)) space. - Edge cases: empty string, all identical characters (
"bbbb"gives 1), all distinct (whole string), and spaces or Unicode counting as ordinary characters. A set-based window that shrinks one step at a time is also O(n) but does up to 2n steps.
Worked solution 20 min
Sliding window with last-seen index
lastremembers where each character appeared most recently;leftis where the current duplicate-free window begins.- For each
right, moveleftjust past the previous copy of the character only if that copy lies inside the window; older indices are ignored. - Record the character's new index, then save the window's start and length whenever it beats the best so far.
- Slice the best window out of
sat the end; the strict>keeps the first of several equally long answers.
def longest_unique_substring(s):
"""Return the first longest substring of s with no repeated characters."""
last = {} # char -> index where it was last seen
left = 0 # start of the current duplicate-free window
best_start, best_len = 0, 0
for right, ch in enumerate(s):
if ch in last and last[ch] >= left:
left = last[ch] + 1 # jump just past the earlier copy
last[ch] = right
if right - left + 1 > best_len:
best_start, best_len = left, right - left + 1
return s[best_start:best_start + best_len]
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Follow-up
- What if up to k distinct characters are allowed? Keep a count map; while it holds more than k keys, shrink from the left, decrementing counts and deleting zeros.
- What if the input is plain ASCII? Replace the dict with a 128-slot array of last-seen indices initialized to -1 for constant-time, cache-friendly lookups.
- How would you return every longest substring? Collect start indices whenever the window length ties the best, and reset the list when a longer window appears.
Discuss the use of data structures in solving algorithmic problems.
Discuss the use of data structures in solving algorithmic problems.
Approach
- The core point: picking a data structure means picking which operations are cheap. List the operations the problem needs (lookup by key, min or max, ordering, prefix search, connectivity), find the bottleneck operation, and choose the structure that makes it fast.
- Know the costs: array O(1) index, O(n) middle insert; hash map or set O(1) average lookup and insert, no sorted order; binary heap O(log n) push and pop, O(1) peek; balanced BST O(log n) insert, delete and floor lookup, O(log n + k) for a k-item range query; stack and queue O(1) push and pop.
- Map patterns to structures: hash map for seen-before or counting (two-sum, anagrams); heap for top-k or next-smallest (Dijkstra); stack for matching brackets; monotonic stack for next-greater element; queue for BFS shortest paths in unweighted graphs; trie for prefixes; union-find for connectivity.
- Show the trade-off on one concrete problem: detecting duplicates with nested loops is O(n^2) time; a hash set is O(n) time but O(n) space; sorting in place first is O(n log n) time with little extra memory. Always name what you gave up.
- Combine structures when one is not enough: a sliding-window maximum pairs the array with a monotonic deque for O(n) total; top-k frequent words pairs a hash map of counts with a size-k min-heap for O(n log k).
- Precision traps: hash map O(1) is average, not worst case, since collisions degrade it; Python
list.pop(0)is O(n), so queues belong incollections.deque;heapqis a min-heap, so negate keys to get max-heap behavior.
Follow-up
- How would you design an LRU cache? A hash map from key to a node in a doubly linked list ordered by recency; get and put move the node to the front in O(1), and eviction removes the tail.
- When would you choose a balanced BST over a hash map in Python? When order matters, e.g. the first event after a timestamp; the standard library has no balanced BST, so use
bisecton a sorted list (O(n) insert) orsortedcontainers. - How do you track the median of a stream? A max-heap for the lower half and a min-heap for the upper half, rebalanced so sizes differ by at most one: O(log n) insert, O(1) median.
Discuss trade-offs between SQL and NoSQL databases in a specific scenario.
Discuss trade-offs between SQL and NoSQL databases in a specific scenario.
Approach
- Anchor the discussion in one stated scenario, e.g. an e-commerce platform with orders and payments, a catalog of products with varied attributes, and a high-volume user activity log. Trade-offs argued in the abstract are the weak answer; give each workload its own verdict.
- Relational (PostgreSQL, MySQL) gives a declared schema, constraints, joins and multi-row ACID transactions. It fits orders and payments: decrementing stock and inserting the order must commit atomically, and foreign keys and
UNIQUEconstraints stop bad data at write time. - NoSQL is several models: document (MongoDB) for nested records, key-value (Redis, DynamoDB) for lookups by key, wide-column (Cassandra) for heavy writes partitioned by key, graph for traversals. The activity log fits wide-column: append-only, read by
user_idand time, needing write scale-out. - The real trade-offs: query flexibility (ad hoc joins in SQL versus tables designed per access pattern in NoSQL, where a new query may need a new copy), consistency (strong by default versus often tunable or eventual), and scaling (replicas and manual sharding versus built-in partitioning).
- Misconceptions to avoid: NoSQL is not schemaless, the schema moves into application code; SQL does scale far with partitioning and distributed SQL such as CockroachDB; many NoSQL stores now offer transactions. Default to relational unless an access pattern clearly demands otherwise.
Follow-up
- Does a catalog with hundreds of category-specific attributes force NoSQL? No; a PostgreSQL
JSONBcolumn with a GIN index keeps flexible attributes beside relational columns. - How would you model the activity log in Cassandra? Partition key
(user_id, month)bounds partition size, clustered by event time descending; "latest events" reads the current month first, then the previous month if it runs short. - How would you report across both stores? Stream changes from each via change data capture into a warehouse and query there, instead of joining operational databases in application code.
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
Explain the differences between REST and SOAP.
Explain the differences between REST and SOAP.
Approach
- SOAP is a protocol: every message is an XML
Envelope(optionalHeader, requiredBody), usually described by an optional WSDL contract and sent via HTTP POST, though transport-independent. REST is an architectural style: URI-named resources, HTTP's uniform interface, stateless requests. - Contract: WSDL plus XSD lets tools generate strongly typed clients and validate every message; REST mandates no contract, and OpenAPI fills that gap by convention. Errors: SOAP returns a standard
Faultelement, REST uses HTTP status codes like 404, 409, 503. - Built-in features: SOAP has the WS-* stack, notably WS-Security (message-level signing and encryption that survives intermediaries), WS-ReliableMessaging and WS-AtomicTransaction. REST leans on the transport: TLS, OAuth 2.0, idempotent verbs, and HTTP caching of GET via
Cache-ControlandETag. - When each fits: SOAP for enterprise or legacy integrations that need a formal contract, message-level security, or a partner that already publishes a WSDL. REST for web, mobile and public APIs where small JSON payloads, cacheability and universal client support matter.
- The misconception to avoid: "REST means JSON." SOAP messages are always XML, but REST is format-agnostic (JSON, XML, HTML, protobuf), a set of constraints rather than a protocol; many "REST" APIs are plain HTTP+JSON without hypermedia (HATEOAS). SOAP is not inherently stateful either.
Follow-up
- How would you version each? REST usually versions in the URI (
/v2/) or a media-type header; SOAP publishes a new namespace and WSDL and keeps the old endpoint alive until clients migrate. - Where do gRPC and GraphQL fit? gRPC is contract-first like SOAP (protobuf IDL, binary over HTTP/2) but far lighter; GraphQL exposes one endpoint where clients pick fields, trading plain HTTP caching for query flexibility.
- Why does idempotency matter for REST clients? GET, PUT and DELETE can be retried safely after a timeout; POST cannot, so retried creates need an idempotency key to avoid duplicates.
What are the principles of object-oriented programming?
What are the principles of object-oriented programming?
Approach
- Name the four pillars, then define each with an example: encapsulation, abstraction, inheritance, polymorphism. Listing the four words without definitions or examples is a weak answer.
- Encapsulation bundles state with the methods that change it and hides the internals so invariants live in one place, e.g.
Account.withdraw()refuses overdrafts instead of callers editingbalance. Abstraction exposes what an object does, not how, e.g. aPaymentGateway.charge()interface. - Inheritance lets a subclass reuse and extend a base class in an is-a relationship. Polymorphism lets one call site run different implementations: subtype polymorphism via overriding, resolved at runtime, plus overloading at compile time and generics (parametric polymorphism).
- Separate the two most-confused terms: encapsulation is about hiding and protecting state; abstraction is about choosing the essential interface. Also note that polymorphism does not require inheritance: interfaces and duck typing provide it too.
- Show design judgment: prefer composition when the relationship is not truly substitutable. The Liskov Substitution Principle is the test; a
Squaresubclass ofRectanglebreaks callers that set width and height independently. SOLID builds on these ideas.
Follow-up
- Abstract class or interface? An abstract class can hold instance state and constructors, and a class extends only one; a class can implement many interfaces, which since Java 8 and C# 8 may carry default methods but still no instance state.
- Why favor composition over inheritance? Subclasses depend on the parent's internals (the fragile base class problem); composition lets you swap behavior at runtime and keeps each class small.
- How does Python encapsulate without
private? By convention (_name) and name mangling for__name; nothing is truly hidden, so invariants rely on properties and validation in methods.
Can you explain the concept of microservices architecture?
Can you explain the concept of microservices architecture?
Approach
- Definition: an application built as a set of small, independently deployable services, each owning one business capability and its own data, talking over the network via HTTP, gRPC or messaging. The contrast is a monolith: one deployable unit, usually one shared database.
- Defining properties: boundaries follow business domains (bounded contexts from domain-driven design); database per service, so no service reads another's tables; each service deploys and scales on its own; one team owns a service end to end.
- Benefits: independent release cadence, fault isolation when paired with timeouts and circuit breakers, scaling only the hot components, smaller codebases per team. Costs: network latency and partial failure, no cross-service ACID transactions, and heavy operational overhead.
- Required infrastructure: an API gateway, service discovery, containers and an orchestrator such as Kubernetes, centralized logs, distributed tracing with correlation IDs, per-service CI/CD, and contract tests so one team cannot silently break another's API.
- The misconception to call out: services that share a database or must deploy together form a distributed monolith, with all the costs and none of the benefits. Microservices mainly solve organizational scaling; a small team is usually better off with a modular monolith first.
Follow-up
- How do you keep data consistent across services? A saga of local transactions with compensating actions, plus a transactional outbox: the event commits with the data and is relayed at least once, so consumers must be idempotent.
- Synchronous or asynchronous calls? Use REST or gRPC when the caller needs an answer now; use events for workflows, which decouples availability at the price of eventual consistency.
- How would you break up an existing monolith? Apply the strangler fig pattern: route one bounded context at a time to a new service behind a facade, moving its data ownership before starting the next.
Design a URL shortening service. What considerations would you take into account?
Design a URL shortening service. What considerations would you take into account?
Approach
- Requirements:
POST /urls {long_url, alias?, expires_at?}returns a short code;GET /{code}redirects. Assume about 100M new links a month and a read:write ratio near 100:1, so redirects dominate and need low latency and high availability. - Code generation: 7 base62 characters give 62^7, about 3.5 trillion codes. Encoding a unique ID in base62 (a counter handed to each app server in blocks) never collides; hashing the long URL needs collision handling, and random codes need a uniqueness check on insert.
- Data model:
urls(code PK, long_url, owner_id, created_at, expires_at). Access is pure key lookup, so a key-value or wide-column store (DynamoDB, Cassandra) or a relational table partitioned bycodeworks. Indexlong_urlonly if identical URLs should reuse one code. - Read path: Redis in front of the database absorbs most redirects, since popularity follows a power law; each redirect emits a click event to Kafka asynchronously. A CDN can cache redirects too, but its hits never reach the service: count them from CDN logs or send
Cache-Control: private, no-store. - Key tradeoff: 301 is cached by browsers, cutting load but hiding repeat clicks and making the target hard to change; 302 keeps every hit visible. Also rate-limit link creation and screen submitted URLs for malware and phishing.
- Failure modes: the ID allocator must not be a single point of failure, so servers pre-fetch ranges and keep working through an outage; a lost cache node should fall back to the database without a stampede (request coalescing); a background job purges expired links.
Follow-up
- How do you stop two users claiming the same custom alias? Insert with a uniqueness constraint or conditional put and return 409 on conflict; check-then-insert races.
- How do you make codes hard to guess? Counter-based codes are sequential and enumerable; apply a reversible permutation to the ID before encoding, or use random codes with a uniqueness check.
- How would you report clicks per day per link? Stream redirect events, aggregate them into daily counters in a separate store, and serve reports from there, never from the redirect path.
How would you architect a system to handle real-time data processing?
How would you architect a system to handle real-time data processing?
Approach
- Interpretation: ingest a continuous event stream (clicks, sensor readings, transactions) and produce aggregates, alerts or enriched records within seconds. Pin down peak rate (assume ~100k events/s), latency target, whether results must be exact, and whether events arrive late or out of order.
- Pipeline: producers write to a durable, partitioned log (Kafka or Kinesis); a stream processor (Flink, Kafka Streams, Spark Structured Streaming) consumes it; results land in a serving store (Redis, Cassandra, or OLAP such as ClickHouse or Druid), with raw events archived to S3 for replay.
- Partition by entity key (user or device ID) so each key's events are processed in order by one worker; parallelism is capped by partition count. Watch for hot keys and salt or pre-aggregate them.
- Use event time with watermarks, not processing time, so tumbling, sliding and session windows stay correct when data is late. Decide the allowed lateness and route events that arrive after it to a side output instead of silently dropping them.
- Guarantees: the processor checkpoints operator state and source offsets together; end-to-end exactly-once also needs idempotent or transactional sinks (upserts keyed by event ID, Kafka transactions). Otherwise accept at-least-once and deduplicate downstream.
- Failure and load: consumer lag is the key health metric; backpressure slows reads instead of dropping data; after a crash the job restores its checkpoint and replays from the log. Retaining the log lets you reprocess history after a bug fix, the kappa approach.
Follow-up
- Lambda or kappa architecture? Lambda runs a batch layer and a speed layer and merges them, duplicating logic; kappa keeps one streaming path and replays the log to reprocess, simpler when retention is long enough.
- How do you absorb a sudden 10x spike? The log buffers it; scale consumers up to the partition count and watch lag. Provision partitions ahead, since adding them later remaps keys to partitions.
- How do you enrich events with reference data? Keep the reference table as local processor state fed by a compacted topic or broadcast stream, rather than calling a database synchronously for every event.
Explain how you would ensure high availability in your system design.
Explain how you would ensure high availability in your system design.
Approach
- Start from a target: an availability SLO (99.9% allows about 8.8 hours of downtime a year, 99.99% about 53 minutes) plus RTO and RPO. Each extra nine usually costs another layer of redundancy, so the target drives the design.
- Remove single points of failure layer by layer: stateless application servers behind a load balancer spread across multiple availability zones, health checks that eject bad instances, autoscaling for load spikes, and redundant load balancers and DNS.
- State is the hard part: replicate the database with automated failover, a synchronous standby in another zone for near-zero RPO and async replicas for reads or other regions. The tradeoff is write latency versus losing recent writes on failover. Test backups by restoring them.
- Contain failures: timeouts on every remote call, retries with exponential backoff and jitter, circuit breakers, bulkheads, and graceful degradation such as serving cached or partial results when a non-critical dependency is down. Make operations idempotent so retries are safe.
- Most outages start with a change, so deploy with canary, rolling or blue-green releases and automatic rollback, and use feature flags. If losing an entire region must be survivable, add a second region as active-passive or active-active.
- Prove it works: alert on SLO burn rate, keep runbooks current, and run regular failover drills or chaos experiments. A failover path that has never been exercised should be assumed broken.
Follow-up
- Active-active or active-passive across regions? Active-active serves traffic in both and must resolve conflicting writes; active-passive is simpler, but failover takes time and the standby must be exercised to stay trustworthy.
- How do you avoid split-brain in database failover? Elect the primary through a consensus store such as etcd or ZooKeeper, and fence the old primary so it cannot accept writes.
- What is the availability of two serial dependencies at 99.9% each? About 99.8%, since serial availabilities multiply; this is why critical paths should have as few hard dependencies as possible.
Describe the steps you would take to migrate an application to the cloud.
Describe the steps you would take to migrate an application to the cloud.
Approach
- Assess first: inventory components and hidden dependencies (databases, file shares, cron jobs, hardcoded IPs, licenses), data volume and a performance baseline, and agree on the goal (cost, elasticity, exiting a data center), because the goal picks the strategy.
- Choose a strategy per component: rehost (lift and shift VMs), replatform (e.g. move the database to a managed service), refactor (containers or serverless), repurchase (SaaS), retain, or retire. A common path is rehost or replatform first and refactor once running in the cloud.
- Build the landing zone before moving anything: account structure, networking (VPC, subnets, VPN or a dedicated link back on-premises), IAM with least privilege, encryption, central logging, all defined as infrastructure as code (e.g. Terraform) so environments are reproducible.
- Data is the risky part: do an initial bulk copy, then continuous replication via change data capture to keep the target in sync, validate with row counts and checksums, and cut over during a short write freeze. Very large file sets may need an offline transfer appliance.
- Migrate in waves, least critical first, testing function, performance and failure behavior. Cut over by switching DNS with a low TTL set in advance, and replicate cloud writes back on-premises (reverse CDC) until confidence is high; otherwise a rollback loses every write made after cutover.
- Optimize afterwards: right-size instances, enable autoscaling, buy committed-use or reserved capacity for steady load, set cost alerts and budgets, and decommission the old environment only after the rollback window closes.
Follow-up
- How do you handle data residency or compliance requirements? Pick regions that satisfy residency rules, encrypt data at rest with customer-managed keys, and use only services the provider certifies for your regulation, such as PCI DSS.
- Lift and shift or refactor? Lift and shift is fastest and least risky but carries inefficiencies and often costs more; refactor the components that need elasticity once they are already in the cloud.
- What most often breaks after migration? Latency between components split across on-premises and cloud, hardcoded hostnames or IPs, and data egress charges nobody budgeted for.
You are given a dataset with inconsistent data entries. How would you clean it?
You are given a dataset with inconsistent data entries. How would you clean it?
Approach
- Interpretation: a tabular dataset (say customer records) with mixed formats, duplicates and gaps. Profile before fixing: per-column types, null rates, distinct values, min and max, and pattern frequencies, so you know which inconsistencies exist and how widespread they are.
- Standardize with explicit rules: trim whitespace and normalize case and Unicode; parse dates to ISO 8601 in one timezone; convert units and currencies; map variants like "NY", "N.Y." and "New York" to one canonical value through a lookup table; validate emails and phone numbers with proper parsers.
- Treat missing and invalid values deliberately: distinguish NULL, empty string and sentinels such as
-1or'N/A'; then drop, impute or flag based on how the data will be used, and record each choice. Silent imputation that skews later analysis is the trap. - Deduplicate in two passes: exact duplicates first, then fuzzy matches on normalized keys (e.g. normalized name plus email, or edit distance), with a clear survivorship rule for which record wins. Keep the merge mapping so merges can be audited or undone.
- Make it repeatable and safe: never overwrite the raw data; implement cleaning as a versioned, idempotent script or pipeline (pandas, SQL, dbt), log rejected rows with the reason, and add validation checks on schema, ranges and uniqueness so new bad data is caught at the source.
Follow-up
- What if new data arrives daily? Validate at ingestion with data-quality checks that quarantine bad rows and alert, instead of cleaning in bulk after the fact.
- When should you drop rows rather than impute? When missingness is rare and random; if it correlates with the outcome, dropping biases results, so flag it and investigate.
- How do you prove the cleaning did not destroy real data? Compare row counts and distributions before and after, and hand-check a random sample of changed records.
Present a solution for a common performance issue in web applications.
Present a solution for a common performance issue in web applications.
Approach
- Pick one issue and go deep. The N+1 query problem is a strong choice: a page lists 50 orders, the ORM fetches them in one query, then lazily fires one more query per order to load its customer, so 51 round trips replace 1 or 2.
- Detect it: query logs or APM traces show dozens of near-identical
SELECT ... WHERE id = ?statements per request, and the query count grows with the number of rows displayed. That scaling pattern is the signature. - Fix by eager loading: a
JOIN, or one batchedSELECT ... FROM customers WHERE id IN (...)over the orders' customer IDs (Djangoselect_related/prefetch_related, Railsincludes, JPAJOIN FETCH), so the query count stays constant. In GraphQL, a DataLoader batches lookups per request. - Complement the fix: paginate so lists stay bounded, make sure each batched lookup hits an index (customers by primary key already does; children such as
order_items WHERE order_id IN (...)need an index onorder_id), and cache hot, rarely changing reference data. - Prove the gain: compare queries per request and p95 latency before and after under the same load, and add a test that fails if the endpoint exceeds a fixed query budget so the problem cannot silently return.
Follow-up
- When is a JOIN worse than a separate IN query? For one-to-many with many children, a JOIN repeats the parent's columns on every child row; a second batched query avoids that row explosion.
- What other web performance issue is common besides N+1 queries? Oversized front-end assets: fix with compression, code splitting, long-lived cache headers and a CDN, measured with Core Web Vitals.
- What if the N+1 happens across services, one HTTP call per item? Add a batch endpoint that accepts many IDs, or cache the referenced data locally, so one request replaces many.
How would you handle a situation where system requirements are constantly changing?
How would you handle a situation where system requirements are constantly changing?
Approach
- Interpretation: requirements keep shifting mid-project. First learn why: unclear goals or misaligned stakeholders call for alignment, while genuine learning from users calls for faster iteration. Treating both the same way fails.
- Process: short iterations with a demo each cycle so changes surface early and cheaply, one prioritized backlog with a single decision owner, and explicit trade-offs: a new requirement in means something else out or the date moves.
- Anchor on stable goals: write down the problem and success metrics, and separate the why (stable) from the how (negotiable), so each change is judged against the goal instead of accepted by default.
- Design for change where it is known: isolate volatile parts behind interfaces or modules, keep frequently changing business rules in configuration, and ship alternatives behind feature flags. Avoid speculative generality; abstract only at confirmed change points.
- Engineering safety net: strong automated tests and CI/CD make refactoring cheap; versioned APIs and schemas keep evolving requirements from breaking consumers; short decision records make churn visible to everyone.
Follow-up
- A stakeholder wants a change a week before launch; what do you do? Estimate the impact, offer options (ship as is, slip the date, ship it behind a flag later) and let the owner decide with the cost visible.
- How do you estimate when requirements keep changing? Estimate only the next iteration in detail, give later work as ranges, and re-forecast each cycle from actual throughput.
- How do you track scope creep? Keep a change log against the original scope with the effort each change added, and review it with stakeholders at each milestone.
Describe how you would optimize a slow-performing application.
Describe how you would optimize a slow-performing application.
Approach
- Interpretation: an existing application is slow for users. Define slow with numbers first: which endpoint or screen, p50 versus p95 and p99 latency, since when, and the target (e.g. p95 under 300 ms). Without a baseline no fix can be proven.
- Localize before optimizing: distributed tracing or APM splits request time across application code, database, external calls and network; host metrics show CPU, memory and GC, disk I/O and connection-pool waits. If 80% of the time is in the database, code micro-tuning is wasted effort.
- Rank likely causes: slow or repeated queries (missing index, N+1, full scans; confirm with
EXPLAINand the slow-query log), synchronous calls to slow dependencies, lock or pool contention, memory pressure and GC pauses, and finally algorithmic hot spots found with a CPU profiler or flame graph. - Fix the largest measured bottleneck first: add the index or rewrite the query, batch or parallelize independent calls, cache read-heavy results with a clear invalidation rule, push non-critical work to a background queue, paginate large responses. Add servers only after per-request cost is sane.
- Verify one change at a time: re-measure the same p95 and p99 after each fix, because stacked changes hide which one helped or hurt. Then keep a per-endpoint p95 latency alert so the next slowdown is caught from metrics, not from user complaints.
Follow-up
- What if CPU is low but latency is high? The service is waiting, not computing: look for I/O waits, lock contention, exhausted connection pools or a slow downstream call in the traces.
- Cache or fix the query? Fix the query when it is cheap to fix; cache when reads far outnumber writes and some staleness is acceptable, because every cache adds invalidation risk.
- Why watch p99 rather than the average? Averages hide the tail, and a page that fans out to many calls is as slow as its slowest call, so tail latency drives what users feel.
How would you approach identifying the root cause of a software bug?
How would you approach identifying the root cause of a software bug?
Approach
- Reproduce first: capture exact inputs, environment, version and steps, then shrink them to the smallest failing case, ideally an automated test. A reliably reproducible bug is mostly solved; an intermittent one points at concurrency, timing or environment differences.
- Gather evidence before guessing: read the full error and stack trace, pull logs around the timestamp by request ID, check metrics, and ask what changed: recent deploys, configuration, dependency upgrades, data shape or traffic.
- Narrow systematically:
git bisectbetween the last good and first bad version, binary-search the input or the code path, and diff a working case against a failing one until the difference that matters is isolated. - Test one hypothesis at a time with a debugger, extra logging or assertions, predicting what you should see before you look. Separate symptom from cause: a null dereference is the symptom; why the value was null (a race, missing validation upstream) is the cause. Asking "why" repeatedly helps.
- Fix at the root, add a regression test that fails before the fix and passes after, search for the same pattern elsewhere, and verify in the environment where it happened. If users are hurting meanwhile, mitigate first with a rollback or feature flag.
Follow-up
- How do you debug something you cannot reproduce locally? Add targeted logging or tracing in production, capture the failing request's inputs, and compare config, data volume and concurrency.
- How do you debug a bug that vanishes when you add logging or attach a debugger? Treat it as a timing or race problem: use low-overhead tracing, stress it with many parallel runs, and audit shared mutable state.
- How do you know you found the root cause? The explanation accounts for every symptom, and reverting the fix brings the bug back.
Built from the rounds and topics Elevi Associates candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Elevi Associates loop
- Write out the reported sequence: Initial Screening, Technical Assessments, Behavioral Interviews, Final Interviews.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.
02Work Python
- Spend the session on Python, which Elevi Associates candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
03Work Apache NiFi / NiFi (NiagaraFiles)
- Spend the session on Apache NiFi / NiFi (NiagaraFiles), which Elevi Associates candidates report being tested on.
- Write one worked example in Apache NiFi / NiFi (NiagaraFiles) and time yourself on it.
Deliverable: One timed worked example in Apache NiFi / NiFi (NiagaraFiles).
04Work Kubernetes
- Spend the session on Kubernetes, which Elevi Associates candidates report being tested on.
- Write one worked example in Kubernetes and time yourself on it.
Deliverable: One timed worked example in Kubernetes.
05Answer out loud: Technical / Domain Questions
- Answer aloud, timed: Explain the differences between REST and SOAP.
- Answer aloud, timed: What are the principles of object-oriented programming?
Deliverable: Spoken answers to 2 reported Technical / Domain Questions question(s), under time.
06Answer out loud: System Design / Architecture
- Answer aloud, timed: Design a URL shortening service. What considerations would you take into account?
- Answer aloud, timed: How would you architect a system to handle real-time data processing?
Deliverable: Spoken answers to 2 reported System Design / Architecture question(s), under time.
07Answer out loud: Behavioral / Leadership
- Answer aloud, timed: Describe a time you faced a significant challenge at work. How did you overcome it?
- Answer aloud, timed: How do you prioritize tasks when working on multiple projects?
Deliverable: Spoken answers to 2 reported Behavioral / Leadership question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Discuss a time when you had to debug a complex issue in production.
Discuss a time when you had to debug a complex issue in production.
Approach
- A strong answer shows methodical debugging under pressure: limit impact first, then test hypotheses against evidence, and keep people informed. Choose an incident whose cause was genuinely non-obvious (intermittent, production-only, load- or data-dependent) and where you drove the diagnosis.
- Stories that backfire: a "complex" issue that turned out to be a typo found in minutes, one where you shipped guesses to production until something stuck, or one where someone else found the cause while you watched. Never blame the colleague who wrote the bug.
- Beats to hit: who was affected and how it was detected; the mitigation you applied before knowing the cause (rollback, feature flag, failover, scaling); the signals you used (logs, traces, metrics, reproducing with production-like data); one hypothesis you ruled out and why.
- Name the root cause in one precise technical sentence, then the fix and the prevention: the test or alert that stops this class of bug, and the postmortem you shared. e.g. "a cache-invalidation race visible only above 200 req/s, reproduced with a load test, fixed with versioned keys."
- Quantify: duration and scope of impact (users, requests, revenue), time to mitigate versus time to root cause, and the after-state, e.g. error rate back from 4% to baseline with no recurrence in the following months.
Follow-up
- What would you do differently next time? Give one concrete change, such as an alert that would have fired earlier or a staging dataset that reproduces production's shape.
- How did you communicate during the incident? Describe the channel, the update cadence, who you kept informed and who made the rollback or fix decisions.
- How did you know the fix worked? Point to the metric that returned to baseline and how long you watched it, plus the test that fails on the old code.
Describe a time you faced a significant challenge at work. How did you overcome it?
Describe a time you faced a significant challenge at work. How did you overcome it?
Approach
- Aim to show ownership and judgment when something hard landed on you, and how you recovered. Pick a challenge with real stakes where your decisions changed the outcome; a technical or delivery problem keeps the focus on those decisions better than an interpersonal one.
- Weak picks: a "challenge" that was only long hours, one caused by your own carelessness with no lesson drawn, a weakness disguised as a strength, or a team win where your personal contribution stays vague behind "we".
- Set up the stakes in two sentences (what would break, for whom, by when) and what made it hard: a tight deadline, unfamiliar technology, missing information or conflicting requirements. Then name the options you weighed and why you chose one.
- Walk through the actions you personally took, including anything you had to learn quickly and anyone you had to persuade, then the outcome and one thing you would now do differently. e.g. "a migration running two weeks late; I automated record reconciliation and we shipped on the original date."
- Quantify the gap you closed: how far off track the work was when you stepped in (days behind, failing jobs, error rate) versus where it finished, and the scale involved (records, users, services).
Follow-up
- What if your approach had failed? Name the fallback you had in mind and the point at which you would have escalated.
- Who else helped, and how did you get their time? Name the roles, what you asked for and how you kept them informed.
- What did you learn that you still use? One specific habit, such as spiking unknowns in week one or writing decision criteria before choosing.
How do you prioritize tasks when working on multiple projects?
How do you prioritize tasks when working on multiple projects?
Approach
- Show a repeatable way of deciding what matters, rather than working on whatever is loudest, and that you make trade-offs visible instead of silently letting work slip.
- State your framework concretely: rank by impact and urgency (an Eisenhower matrix or impact versus effort), put work that unblocks others first, respect hard deadlines, and learn relative impact from your manager and the project goals rather than guessing.
- Describe the mechanics: one visible list or board, large tasks broken into shippable pieces, time blocks for focused work, and re-prioritizing on a fixed cadence (daily or weekly) as new information arrives.
- The beat that matters most: when two priorities genuinely conflict, raise it early with options ("A by Friday and B next Wednesday, or B first") and let the owner of the goals decide. Saying yes to everything is the answer that backfires.
- Close with one short example with numbers, e.g. "two launches and an on-call week; I deferred a low-impact refactor, told both product managers the dates, and both launches shipped on time."
Follow-up
- What if your manager and another team both claim top priority? Put both requests and deadlines in front of your manager, propose an order and get an explicit decision.
- How do you handle urgent interruptions such as production issues? Handle true emergencies immediately, log everything else to the backlog, and re-plan displaced work with stakeholders.
- How do you know you prioritized well? Look back: did the high-impact items ship, did anything critical slip, and was any stakeholder surprised?
Give an example of a time when you had to collaborate with a difficult team member.
Give an example of a time when you had to collaborate with a difficult team member.
Approach
- Show empathy, directness and that you kept delivering while a working relationship was strained, without blaming. Choose a real friction over work (reviews, ownership, responsiveness, design approach), not a clash of personalities.
- Avoid casting the colleague as the villain, escalating to get them removed as your first move, or avoiding the conflict until the work suffered. Describe their behavior neutrally and factually.
- Beats: the specific behavior and its effect on the work; how you tried to understand their side, usually a private conversation asking what was driving it (deadline pressure, unclear ownership); and what you changed in your own approach.
- Show the resolution: a concrete agreement such as a design review before coding, a response-time norm for reviews or split ownership, with escalation only if needed and done openly. Describe where the relationship ended up, even if it was just a workable truce.
- Quantify the effect on the work, e.g. "reviews sat four days; after a one-on-one we agreed on a weekly design sync and turnaround dropped under a day." Delivery dates met and fewer reopened tickets also count.
Follow-up
- What if talking directly had not worked? Explain when you would involve your manager: with specific examples, framed around impact on the work, after telling the colleague you would.
- Was any of the friction your fault? Name something real you adjusted, like the tone of your review comments; answering no signals low self-awareness.
- How do you work with someone whose style differs from yours? Agree explicit norms (channel, response times, who decides) so style differences stop causing friction.
What strategies do you use to manage stress during high-pressure projects?
What strategies do you use to manage stress during high-pressure projects?
Approach
- Show self-awareness and sustainability: that you stay effective and keep quality up under a deadline without burning out or pushing stress onto the team. Claiming you never feel stress is not credible.
- Lead with work strategies, not only self-care: break the project into small milestones, find the critical path, negotiate scope cuts early, and keep a written task list so nothing lives only in your head.
- Show how quality survives pressure: you keep tests and reviews on risky changes, ship behind feature flags so rollback is cheap, and write clear hand-offs. Rushed mistakes usually cost more time than the shortcut saved.
- Cover the personal side briefly and believably: protecting sleep, short breaks, and saying out loud when the load is unsustainable. Raising it early with your manager reads as maturity, not weakness.
- Anchor it in one real crunch, e.g. "a launch moved up two weeks; I split work into daily goals, moved two features to a follow-up release, and we shipped with no critical bugs." Quantify the deadline, the scope and the result.
Follow-up
- What do you do when the whole team is stressed? Make the workload visible, cut scope together, share the heaviest tasks and keep stand-ups focused on blockers.
- Have you missed a deadline under pressure? Own it: say when you flagged the risk, what you delivered instead and what you changed afterwards.
- How do you tell real urgency from felt urgency? Ask what concretely happens if the task slips a day; if nothing measurable, it is not an emergency.
How do you handle feedback on your work?
How do you handle feedback on your work?
Approach
- Show coachability: that you seek feedback, separate it from your identity and actually change behavior, and that you can push back respectfully when feedback is wrong.
- Describe your process: listen fully without defending, ask for specific examples, restate the point to confirm you understood, decide what you will change, then follow up later to check whether the change landed.
- Give a real example where the feedback stung and you acted on it, e.g. "a reviewer said my pull requests were too big to review; I kept them under about 400 lines and review time roughly halved." A story beats a statement of values.
- Cover disagreement: when feedback seems wrong you discuss it with evidence, stay open to being the one who is wrong, and commit once a decision is made. "I accept all feedback" sounds hollow; getting defensive with no fix sounds risky.
- Show you seek it proactively: requesting early review of design docs, asking for specific feedback in one-on-ones, and giving feedback to peers in the same constructive way.
Follow-up
- How do you act on vague feedback such as "be more proactive"? Ask for one or two recent examples and what doing it well would have looked like, then agree how you will both judge progress.
- How do you give critical feedback to a peer? Privately and promptly, about specific behavior and its impact, with a suggestion rather than a verdict.
- What is the most useful feedback you have received? Pick one that changed a habit and describe the before and after.
Discuss your methodology for conducting a code review.
Discuss your methodology for conducting a code review.
Approach
- Show that your reviews improve correctness and the team, not just formatting, and how you balance thoroughness, speed and tone. Give an actual order of operations rather than "I look for bugs".
- First pass, context and design: read the description and linked ticket, understand the intended behavior, and check the size; ask for a split if it is too large to review well. Then ask whether the approach fits the codebase and whether something simpler would do.
- Second pass, correctness and risk: edge cases and error handling, concurrency, security (input validation, authorization, secrets), performance on realistic data, backward compatibility of APIs and migrations, and whether the tests would fail without the change.
- Leave style to linters and formatters. Label comments by severity (blocking versus nit), explain the why, ask questions rather than give orders, and approve with minor comments to save a round trip. Check out and run the branch when behavior is not obvious from the diff.
- Mention speed and your role as an author: review within a working day, keep your own pull requests small with a clear description, and self-review the diff before requesting others.
Follow-up
- What if you and the author disagree and neither budges? Move to a short call, fall back on documented team conventions or ask a third reviewer; do not let the PR stall in comments.
- How do you review a 2,000-line pull request? Ask to split it; if you cannot, review commit by commit and start with interfaces, migrations and tests.
- What is the most important issue you caught in review? Pick a real correctness or security bug and explain what in your process surfaced it.
- 01
Discuss a time when you had to debug a complex issue in production.
- 02
Describe a time you faced a significant challenge at work. How did you overcome it?
- 03
How do you prioritize tasks when working on multiple projects?
- 04
Give an example of a time when you had to collaborate with a difficult team member.
What is the typical timeline from initial screen to offer?
The timeline can vary based on the role and team, but generally, candidates can expect the process to take 4-6 weeks from the initial application to the final offer.
Elevi Associates Software Engineer candidate reports ↗How much preparation time is recommended for interviews?
A minimum of two weeks of dedicated preparation is advisable. Focus on practicing coding problems, reviewing system design concepts, and preparing for behavioral questions.
Elevi Associates Software Engineer candidate reports ↗What differentiates successful candidates at Elevi Associates?
Successful candidates demonstrate a strong grasp of technical skills, effective problem-solving approaches, and a collaborative mindset. They align well with the company’s values and show enthusiasm for the role.
Elevi Associates Software Engineer candidate reports ↗Can you describe the culture and working style at Elevi Associates?
The culture emphasizes innovation, teamwork, and continuous improvement. Employees are encouraged to share ideas and collaborate across departments to drive projects forward.
Elevi Associates Software Engineer candidate reports ↗How should I handle ambiguous questions during interviews?
When faced with ambiguous questions, take a moment to clarify your understanding. Articulate your thought process and demonstrate how you would approach the problem systematically.
Elevi Associates Software Engineer candidate reports ↗How hard is the Elevi Associates interview?
Candidates most commonly rate Elevi Associates interviews as easy, based on 1 reported interviews.
Elevi Associates Software Engineer candidate reports ↗What topics does Elevi Associates test in interviews?
Elevi Associates interviews most often cover Python, Apache NiFi / NiFi (NiagaraFiles), Kubernetes, ETL (Extract, Transform, Load), and Data ingest / processing / transformation / transport pipelines. The exact emphasis depends on the specific role you apply for.
Elevi Associates Software Engineer candidate reports ↗Where is Elevi Associates headquartered?
Elevi Associates is headquartered in Columbia, US.
Elevi Associates Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Elevi Associates Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22