As a Software Engineer at Lingaro, you are at the intersection of complex data ecosystems and high-impact business solutions. You will be tasked with designing, developing, and maintaining robust software applications that empower global organizations to derive actionable insights from their data. Your role is essential in bridging the gap between raw data infrastructure and user-facing applications, ensuring that the software you build is scalable, efficient, and reliable. This position demands a unique blend of technical precision and strategic thinking. You will frequently collaborate with cross-functional teams, including data architects, analysts, and project managers, to solve multifaceted problems. Whether you are optimizing SQL queries for performance, building responsive front-end interfaces, or managing back-end services, your work directly influences the efficiency and decision-making capabilities of Lingaro’s clients. Success in this role requires not just technical proficiency, but the ability to articulate your logic clearly in a collaborative, team-oriented environment.
Recruiter Screen
reportedInitial conversation with a recruiter to assess fit and discuss the role.
What to demonstrate
- Initial conversation with a recruiter to assess fit and discuss 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 Rounds
reportedOne or more technical interviews focusing on core technical skills.
What to demonstrate
- One or more technical interviews focusing on core technical skills
- Depth in Python
How to prepare
- Answer aloud and timed: What are the key differences between various types of SQL joins?
- Answer aloud and timed: Can you walk me through the steps to optimize a slow-running SQL query?
Managerial/Panel Interview
reportedFinal interview with multiple team members to evaluate cultural fit and technical expertise.
What to demonstrate
- Final interview with multiple team members to evaluate cultural fit and technical expertise
- Depth in Python
How to prepare
- Answer aloud and timed: How do you manage asynchronous operations in JavaScript?
- Answer aloud and timed: How would you design a database schema for a reporting application?
PracHub editorial advice for the preparation topics above.
Think aloud
During coding tasks, explain your thought process. It helps the interviewer understand your problem-solving logic even if you get stuck.
Review your resume
Be prepared to discuss every project you list in detail. Know the "why" behind every technology you chose to use.
Prepare questions
At the end of your interviews, ask insightful questions about the team’s current technical challenges or the company’s development roadmap.
Stay calm under pressure
If you are asked a question you don't know, be honest about it, but pivot to how you would go about finding the answer.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Can you explain the difference between a list and a tuple in Python?
Can you explain the difference between a list and a tuple in Python?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
How do you manage asynchronous operations in JavaScript?
How do you manage asynchronous operations in JavaScript?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
What are the key differences between various types of SQL joins?
What are the key differences between various types of SQL joins?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Can you walk me through the steps to optimize a slow-running SQL query?
Can you walk me through the steps to optimize a slow-running SQL query?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
How do you approach data normalization, and when might you choose to denormalize?
How do you approach data normalization, and when might you choose to denormalize?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
How would you design a database schema for a reporting application?
How would you design a database schema for a reporting application?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
What are the pros and cons of using a relational versus a non-relational database for this specific use case?
What are the pros and cons of using a relational versus a non-relational database for this specific use case?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
What are the core principles of responsive web design?
What are the core principles of responsive web design?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you manage state in a complex front-end application?
How do you manage state in a complex front-end application?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Can you explain the Document Object Model (DOM) and how you manipulate it efficiently?
Can you explain the Document Object Model (DOM) and how you manipulate it efficiently?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Read latency spikes on a sixty-second sawtooth
The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.
Approach
- Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
- Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
- Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
- De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.
Follow-up
- The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
- How does your answer change if the recompute takes 4 seconds instead of 250 ms?
Built from the rounds and topics Lingaro candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Lingaro loop
- Write out the reported sequence: Recruiter Screen, Technical Rounds, Managerial/Panel Interview.
- 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 3 reported rounds, with the weakest marked.
02Work Python
- Spend the session on Python, which Lingaro candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
03Work SQL
- Spend the session on SQL, which Lingaro candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
04Work Technical Interview (General)
- Spend the session on Technical Interview (General), which Lingaro candidates report being tested on.
- Write one worked example in Technical Interview (General) and time yourself on it.
Deliverable: One timed worked example in Technical Interview (General).
05Answer out loud: Technical Foundations and Coding
- Answer aloud, timed: Can you explain the difference between a list and a tuple in Python?
- Answer aloud, timed: How do you handle transaction management in a Java/Spring environment?
Deliverable: Spoken answers to 2 reported Technical Foundations and Coding question(s), under time.
06Answer out loud: Data Analysis and Modeling
- Answer aloud, timed: How would you design a database schema for a reporting application?
- Answer aloud, timed: What are the pros and cons of using a relational versus a non-relational database for this specific use case?
Deliverable: Spoken answers to 2 reported Data Analysis and Modeling question(s), under time.
07Answer out loud: Front-End Development
- Answer aloud, timed: What are the core principles of responsive web design?
- Answer aloud, timed: How do you manage state in a complex front-end application?
Deliverable: Spoken answers to 2 reported Front-End Development 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.
How do you handle transaction management in a Java/Spring environment?
How do you handle transaction management in a Java/Spring environment?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Ship under a deadline and bound the debt you chose
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
Approach
- Name the deferred failure precisely instead of calling it slow. OFFSET n makes the database produce and discard n rows, so cost grows with page depth; without an index matching the sort, every matching row is read and sorted before the limit applies; and rows inserted between two page fetches shift across the boundary so items are skipped or repeated with nothing in the response to signal it.
- Bound the blast radius with something mechanical rather than a promise: cap maximum page depth, cap page size, restrict the endpoint to one internal caller, or keep it behind a flag. State which failure each cap removes and which it leaves standing.
- Attach a number to the trigger and wire it to an alarm: the first tenant crossing N resources, or the endpoint's p99 crossing its share of the 400 ms budget, so the debt announces itself instead of waiting to be remembered.
- Write it where the next engineer looks, which is the code and the ticket, not a chat message: what was deferred, why, the cap, and the trigger.
Follow-up
- At what page depth does the offset version breach your latency budget, given your page size and row counts?
- What breaks first when you switch to keyset pagination later, and what does a client holding an old page token see?
Estimate work you have never done and defend the range
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
Approach
- Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
- Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
- Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
- Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.
Follow-up
- How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
- Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?
- 01
How do you handle transaction management in a Java/Spring environment?
- 02
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
- 03
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
How long should I spend preparing?
Given the technical depth required, we recommend at least 2–3 weeks of focused study, especially if you need to brush up on SQL optimization or language-specific internals.
Lingaro Software Engineer candidate reports ↗Is the interview process mostly technical or behavioral?
It is a balanced mix. You will face rigorous technical challenges, but the final rounds place significant weight on how you communicate your work to managers and peers.
Lingaro Software Engineer candidate reports ↗Can I expect a coding test?
Yes, many candidates report a coding assessment or technical interview as a standard part of the process to ensure you meet the necessary proficiency standards.
Lingaro Software Engineer candidate reports ↗What differentiates successful candidates?
Successful candidates don't just provide the right code—they explain their reasoning, acknowledge trade-offs, and demonstrate curiosity about the business impact of their work.
Lingaro Software Engineer candidate reports ↗What topics does Lingaro test in interviews?
Lingaro interviews most often cover Python, Data Modeling, Problem Solving, Communication, and Google Cloud Platform (GCP). The exact emphasis depends on the specific role you apply for.
Lingaro Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Lingaro Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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