A Software Engineer at Ping Identity plays a critical role in securing the digital world. Ping Identity is an industry leader in Intelligent Identity and Access Management (IAM) solutions, helping the world's largest enterprises secure their users, devices, and APIs. In this role, you will design, build, and deploy highly scalable, resilient, and secure software that manages millions of digital identities in real-time. The work you do directly impacts zero-trust security frameworks, single sign-on (SSO) capabilities, multi-factor authentication (MFA), and directory services that global businesses rely on every single day.
Operating at this scale introduces unique technical challenges. You will work on distributed systems that require ultra-low latency, high availability, and absolute cryptographic security. Whether you are working on the core backend engine, crafting intuitive administrative consoles, or building cloud-native infrastructure, your contributions will help prevent identity fraud and secure enterprise ecosystems.
The engineering culture at Ping Identity is deeply technical, collaborative, and focused on continuous improvement. Teams are structured to encourage ownership, meaning you will not just write code—you will also influence architectural decisions, participate in robust peer reviews, and collaborate across product, security, and operations teams to deliver world-class security products.
Recruiter Call
reportedBefore anything technical happens, someone has to decide which rung of the ladder your loop is calibrated to, and that decision sets the bar for every round after it. It comes from how you describe scope, not from your title, because titles do not convert cleanly between companies. The weak version of the answer is team size and years. The strong version names the largest change you shipped where nobody reviewed the design, what would have broken if you had been wrong, and what you were paged for. Get the level said out loud on this call, because the range and the loop both follow from it.
What to demonstrate
- Whether the scope in your own account maps onto a level the team actually has an opening at, so a mismatch ends the process cheaply rather than after four interviewers have spent a day
- Whether your title needs re-mapping: the same word describes very different amounts of independent decision-making at a twenty-person company and a ten-thousand-person one
- Whether your compensation expectation can be filled at that level in the structure the role pays in, which is why the number gets asked for before any engineer is scheduled
How to prepare
- Write down two changes from the last two years: the largest one you designed with nobody reviewing the design, and the largest one where someone more senior did. Lead with the first when scope comes up, and be ready to say which parts of the second were yours
- Ask which level the loop is calibrated to and what changes at the level above it, then plan your weeks from that answer rather than from the posting
- Settle a total-compensation range beforehand with the split named, base against bonus against equity and its vesting period, so a question about numbers gets a number instead of the word market
Cognitive Assessment
reportedMost of the time lost in this format is not lost to thinking. It goes to a standard-library call you half-remember, an off-by-one in a loop bound, and a debugging loop that mutates code at random until something passes. When output is wrong, stop re-reading the whole function: take the smallest input that reproduces it and walk the state through by hand, printing intermediates if the environment allows. Guessing at a fix without a failing case you understand is how a five-minute bug becomes twenty, and the clock does not pause while you do it.
What to demonstrate
- Whether you reach the right structure without a detour, and can write it from memory rather than only recall that one exists
- Whether overflow is considered where the language has fixed-width integers, since a signed 32-bit value stops at 2,147,483,647 and then wraps in Java, is undefined behaviour in C++, and does not arise in Python, whose integers grow instead
- Whether recursion depth is treated as a constraint on large inputs, given that CPython's default limit is 1000 frames and a deep recursion can exhaust the stack in any language where an iterative version would not
- Whether a failing case is isolated and explained before any edit is made to the code
How to prepare
- From an empty file and with no references open, implement the pieces you lean on most: a heap push and pop, an iterative DFS with an explicit stack, and a binary search whose midpoint is written lo + (hi - lo) / 2, which avoids the overflow that (lo + hi) / 2 can hit in a fixed-width integer type
- Time yourself on the ten library calls you look up most, such as sorting with a custom comparator, splitting and joining strings, and finding the next key at or above a value in an ordered map, until the lookup is gone
- Take a solution you know is broken and, before touching it, write one sentence naming the input, the expected value and the actual value. Repeat until you do it without deciding to.
Technical Screens
reportedInput bounds are the part of the prompt most often skimmed, and they usually contain the answer. They tell you which complexity class is admissible, which narrows the search before you have thought about the problem itself. As a rough planning figure, a compiled language does on the order of 10^8 simple operations per second and an interpreted one roughly an order of magnitude less. So n up to about twenty admits enumerating subsets, a few thousand admits a quadratic pass, and a million admits neither: you need near-linear, or linear with a log factor. If the bounds are missing, ask for them.
What to demonstrate
- Whether the approach is justified by the stated input size rather than by whichever pattern you recognised first
- Whether you ask about the properties that change the algorithm: whether the input arrives sorted, whether duplicates occur, whether values are bounded integers, whether it all fits in memory
- Whether you can name the bottleneck in your own solution and what would remove it, even when you deliberately leave it in place
- Whether a claimed speedup is real, since memoising a recursion only helps when subproblems genuinely overlap and the state can be keyed cheaply
How to prepare
- For each algorithm you rely on, write down the largest n it handles in roughly a second, then check two of those figures by timing them in the language you will actually type in
- For two weeks, write one line naming your target complexity and the bound that justifies it before you write any code, then compare that line with what you ended up submitting
- Practise the conversion backwards: given a required O(n log n), list the mechanisms that get you there (sorting, a heap, an ordered map, divide and conquer) and choose by what the problem needs to query, not by what you used last
Final Panel Interview
reportedWhere the day includes a partner from product, design or data, that conversation is weighted like the technical ones and prepared for least. They are deciding one thing: whether having you in the room makes their decisions cheaper. That means options with costs attached, not implementation detail and not "it depends". An estimate someone can plan against — a range, the assumption that would push it to the high end, and what you would drop to hit the low one — is worth more than a confident single number, which everyone present already knows is wrong.
What to demonstrate
- Whether an estimate comes as a range with the assumption most likely to break it, and states what a specific scope cut would actually buy
- Whether a technical constraint is handed over as a choice with consequences on their side, rather than as a verdict they have no standing to argue with
- Whether you establish what decision is on the table before proposing anything
- Whether risk is raised while it can still change the plan, with the trigger that would confirm it, instead of reported afterwards as a slip
How to prepare
- Take a project that shipped late and write the two-sentence warning you could have given three weeks earlier, naming what you would have needed decided at that point
- Rehearse one estimate out loud until it arrives in three parts: the range, the single assumption that would blow it, and the smallest thing you would cut to protect the date
- Rewrite an objection you have actually made — the "we can't do that" version — as two options with their costs, so the choice ends up with the person who owns it
PracHub editorial advice for the preparation topics above.
Serialising a tenant's writes through select ... for update on a single counter row
It is the first change that makes a counter correct, and it caps that tenant's write throughput at roughly one divided by the lock hold time. A transaction that takes the lock, makes a network call and then commits holds it for the entire round trip: at 2 ms that is about 500 writes per second for the whole tenant, and the largest tenants are exactly the ones that exceed it. The damage then spreads, because every waiter holds a database connection while it queues, so one hot tenant drains the shared pool and the symptom presents as a site-wide latency incident rather than as a lock problem. The repairs are to shrink the critical section to a single statement, to shard the counter into per-(tenant, hour) or per-(tenant, bucket) rows and sum on read, or to batch in memory and flush periodically while accepting the bounded loss that batching implies.
Paginating a growing table with limit and offset
Two unrelated defects share the idiom. Correctness: rows inserted or deleted between page requests shift the window, so a consumer walking an export skips rows and sees others twice, which for a customer-facing sync is silent data loss rather than an error anyone notices. Cost: the database still produces and discards the skipped rows, so page N costs time proportional to N times the page size and a deep page on a large table degrades from milliseconds to seconds. Keyset pagination over a stable, unique, indexed ordering -- where (created_at, id) < ($1, $2) order by created_at desc, id desc limit $3 -- is constant-cost per page and immune to shifting, on the precondition that the cursor columns never change value for a row, which disqualifies updated_at as a cursor.
Listing technologies instead of trade-offs
Name the property the design needs first, such as ordered range scans, multi-entity transactions, cheap appends, or a predictable p99, then pick something that provides it and say what it gives up in exchange. Almost any component is defensible once you state the requirement it satisfies and the one it sacrifices.
Finishing a solution without stating its complexity
Give time and space in the same breath as the code, and define n explicitly when there are two sizes, since n nodes and m edges are not interchangeable. Space is the half that gets skipped: count the auxiliary structures you allocate and the recursion stack at its deepest, not only the answer you hand back.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Walk through how you would debug a complex memory leak in a running pr…
Walk through how you would debug a complex memory leak in a running production application.
Approach
- State the target complexity and say which constraint rules the naive version out.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
Follow-up
- Which test case would catch an off-by-one here?
- How does this change if the input no longer fits in memory?
What are the performance implications of multi-threaded execution in a…
What are the performance implications of multi-threaded execution in a high-throughput backend service?
Approach
- Identify the window where an invariant is briefly untrue.
- Name what is shared across threads and what owns each piece of state.
- Say what the runtime actually does before reasoning about the code.
Follow-up
- How would you prove the race exists rather than suspect it?
- Where could this allocate more than you expect?
Walk through how you would implement a thread-safe cache from scratch …
Walk through how you would implement a thread-safe cache from scratch without using standard library concurrent maps.
Approach
- Distinguish a value from a reference to it, and say which one you handed out.
- Name what is shared across threads and what owns each piece of state.
- Say what the runtime actually does before reasoning about the code.
Follow-up
- How would you prove the race exists rather than suspect it?
- What happens if two callers reach this at the same time?
Locate a billing reconciliation gap without rescanning ninety million events
A tenant's sealed invoice total is 0.4% below the sum of its raw usage_event rows for the period. That tenant has 90 million events over 30 days in a table partitioned daily on ingested_at, and its rollups carry source_max_ingested_at, revision and sealed_at. Recomputing all 30 days from raw is correct, and you are not going to do it. Give the procedure that locates the divergent (workspace, sku, hour) cell, the cost of each probe, and the one query you run before any of it.
Approach
- Run the free query first. Sum raw quantity for the period restricted to
ingested_at <= source_max_ingested_atof the sealed rollups, and compare that against the unrestricted sum. The rollup stores the watermark precisely so this can be answered without a scan. If the whole 0.4% sits above the watermark, nothing is broken: it is late data, it becomes an adjustment line, and the investigation ends in one query. - Only if the gap survives that test do you bisect, and you bisect by dimension rather than by rows. Compare 30 per-day totals, then inside the offending day compare the 6 SKUs, then the workspaces, then the 24 hours. That is roughly 30 + 6 + W + 24 grouped probes, each an indexed range scan over one daily partition for one tenant, against O(N) per attempt for the naive re-fold.
- Quantify why naive is not merely slow but unusable mid-incident: at a generous 200,000 rows/second sequential, 90 million rows is about 7.5 minutes per attempt, you will want ten attempts, and every one competes for I/O on the same partitions live ingest is writing. The diagnostic worsens the backlog it is diagnosing.
- Before fetching each comparison, state what it would look like under each hypothesis. Two adjacent hours off by equal and opposite amounts is
occurred_atversusingested_atbucketing. A whole day offset by exactly N hours is a timezone applied at the wrong layer. A gap confined to one SKU in one workspace is an environment filter. The same(tenant_id, idempotency_key)present in twoingested_daypartitions is the dedup horizon losing a retry that crossed midnight. - Make the next bisection cheap by storing the aggregate you keep recomputing. A per-
(tenant_id, ingested_day)count and quantity checksum turns step two from thirty probes into one read, and it is the same number the reconciliation job already produces. - Whatever you find, the sealed period does not change value. The correction is an adjustment line pointing at the line it reverses, carrying its own
source_rollup_watermark, because the original invoice is the evidence of what the customer was charged.
Worked solution 35 min
- Reproduce the shape locally: generate 2 million events for one synthetic tenant over 5 days, fold them, then inject three defects, namely 0.2% of events bucketed by
ingested_at, a handful of duplicateidempotency_keyvalues whose retries cross midnight, and a block of events ingested after the seal. - Write the watermark-bounded query first and record how much of the gap it explains on its own.
- Bisect by day, then SKU, then hour, recording the probe count and the rows each probe touches.
- For each located cell, write down the predicted signature before querying it, then check whether the data matches the prediction.
- Time a full re-fold of the 2 million rows and extrapolate to 90 million.
Follow-up
- The gap is 0.4% in one direction on one day and 0.4% the other way the next day. What does that shape rule in, and what does it rule out?
- How do you distinguish a duplicate from a restatement, given
revisionandrecomputed_aton the rollup? - Ingest is still running while you investigate. What makes your two numbers comparable at all?
Explain why the metering dashboard scans every daily partition
usage_event is range-partitioned daily on ingested_at and holds tenant_id, workspace_id, environment, sku, quantity numeric(20,6), occurred_at and ingested_at. The only relevant index is on (occurred_at). A dashboard runs select sku, sum(quantity) from usage_event where tenant_id = $1 and date_trunc('hour', occurred_at) >= $2 and environment = 'production' group by sku, and EXPLAIN shows a sequential scan of every partition. Give each distinct reason, rewrite the predicate so an index can serve it, propose the index, and state the write cost its column order adds.
Approach
- Separate the three causes rather than blaming one. First,
date_trunc('hour', occurred_at)wraps the column, so the predicate is not sargable against a btree on the bare column. Second, pruning keys off ingested_at while the query constrains occurred_at, so no partition can be excluded. Third, even made sargable, (occurred_at) is not tenant-leading, so for one tenant among thousands the scan reads the whole time range and discards nearly all of it. - Rewrite the bound carefully, because the obvious rewrite is only conditionally equivalent.
date_trunc('hour', x) >= $2equalsx >= $2only when $2 is already hour-aligned; for an arbitrary $2 it meansx >= date_trunc('hour', $2) + interval '1 hour'. Normalise the parameter in the caller and leave the column bare. - Restore pruning with a second, redundant predicate on the partition key:
ingested_at >= $2 - interval '<late-data horizon>'. State both sides of it. It prunes to a handful of partitions, and it silently omits any event whose ingest lagged past that horizon, which is precisely what a producer replay produces. Either document the horizon as a stated bound, or partition on occurred_at and move the problem into the dedup window instead. - Propose
(tenant_id, occurred_at) include (sku, quantity)per partition. A partial indexwhere environment = 'production'mostly saves size rather than selectivity, since production dominates the three environments; take it if non-production is a meaningful share and skip it otherwise. - Price the write path honestly. At roughly 250M rows/day each extra index is another insert plus WAL per row, and a tenant-leading key scatters inserts across one hot leaf per active tenant instead of appending to a single rightmost leaf, so page dirtying and random I/O both rise. An INCLUDE payload widens every leaf entry and enlarges the index accordingly.
- Add the index-only-scan caveat before someone reports it as a regression: on a freshly appended table the visibility map is not yet set for recent pages, so the INCLUDE columns still cost heap fetches until autovacuum has been through, and the newest hour is exactly the data the dashboard reads.
Worked solution 30 min
- Build 30 daily partitions with skewed tenants, one holding about 40% of the rows, then ANALYZE.
- Run
explain (analyze, buffers)on the original query and record how many partitions were scanned and the rows removed by filter. - Apply the rewritten predicate and the index, re-run, and confirm the plan lists only the partitions inside the ingested_at bound.
- Re-run with $2 set to a non-hour-aligned timestamp and confirm the rewritten and original predicates return identical rows.
- Insert an event with ingested_at six hours past occurred_at and check whether the pruning predicate excludes it.
Follow-up
- CREATE INDEX CONCURRENTLY is not supported on a partitioned parent. Give the sequence that gets this index onto 400 existing partitions without blocking ingest.
- One tenant holds 200 times the median row count and the dashboard still times out for them with the index in place. What changes?
- Should this read hit
usage_rollup_hourlyinstead? State what that costs in freshness and what the watermark lets you promise.
Rebuild an hourly rollup with deduplication and late-arrival accounting
From usage_event (event_id, tenant_id, workspace_id, environment, sku, quantity numeric(20,6), idempotency_key, occurred_at, ingested_at), produce the values usage_rollup_hourly should hold for one tenant over one day: per (workspace_id, sku, hour_start) the deduplicated quantity_sum, event_count and source_max_ingested_at, bucketed by occurred_at. Duplicates share (tenant_id, idempotency_key). Also report, per hour, the running total across the day and the share of quantity that arrived more than two hours after the hour began. Write the query, and state which duplicates a daily unique index cannot catch.
Approach
- Deduplicate in its own CTE before any aggregation, because a SUM cannot be un-summed:
row_number() over (partition by tenant_id, idempotency_key order by ingested_at, event_id) = 1. Include the tiebreaker. Without it the surviving row is non-deterministic when two duplicates share an ingested_at, and a rollup described as deterministically recomputable then disagrees with itself between runs. - Bucket on occurred_at and nothing else, and pin the timezone explicitly.
date_trunc('hour', timestamptz)truncates in the session's TimeZone setting, so the same query run by a session set to a non-UTC zone buckets differently; use the three-argumentdate_trunc('hour', occurred_at, 'UTC')on PostgreSQL 16 or later, ordate_trunc('hour', occurred_at at time zone 'UTC') at time zone 'UTC'before that. Filterenvironment = 'production'explicitly, since metering covers three environments and billing covers one. - Aggregate to the grain with
sum(quantity),count(*)andmax(ingested_at). The last is not decoration: it is the watermark the row consumed up to, and without it there is no way to prove afterwards what a number did and did not include. - Compute the late share inside the dedup-and-aggregate step as a conditional aggregate,
sum(quantity) filter (where ingested_at > hour_start + interval '2 hours'), then divide by the hour's total. Compute the running total as a window over the already aggregated rows:sum(quantity_sum) over (partition by workspace_id, sku order by hour_start rows between unbounded preceding and current row). Running either over raw rows puts the duplicates back. - Answer the index question exactly. The unique constraint is on (ingested_day, tenant_id, idempotency_key), because a unique index on a partitioned table must contain the partition key. It therefore deduplicates only within one ingest day and admits a duplicate whose retry crosses midnight or whose replay runs a week later. That is why this CTE dedups across the whole window being recomputed, and why the dedup horizon is a correctness parameter rather than a retention cost.
- Keep the numeric type all the way through. quantity is numeric so the sums are exact; a cast to double precision anywhere in this pipeline reintroduces drift that surfaces only as a few unreconcilable cents per tenant per month, long after the query is out of anyone's mind.
Follow-up
- A dispute forces the same recompute over 40 days for one tenant. What changes about the dedup CTE's memory use and the chosen plan, and what would you do about it?
- Two runs a minute apart return different quantity_sum values for an hour that is already closed. Give two mechanisms that produce that, and the single query that distinguishes them.
- Express the same rollup incrementally so it does not re-scan the day each time the watermark advances. What does the incremental version stop being able to answer?
How do you design a system to be resilient against sudden spikes in au…
How do you design a system to be resilient against sudden spikes in authentication traffic (e.g., a massive enterprise shift-start)?
Approach
- Name the read and write paths separately; they rarely have the same bottleneck.
- State the consistency you need, and where you are willing to be stale.
- Choose a partition key and say what query it makes expensive.
Follow-up
- What would you drop to keep the system up under load?
- How does this behave when that dependency is down for an hour?
Walk through the process of setting up secure token exchange protocols…
Walk through the process of setting up secure token exchange protocols between an Identity Provider and a Service Provider.
Approach
- Name the failure you are designing for, then the recovery path.
- Choose a partition key and say what query it makes expensive.
- Fix the scope first: who calls this, how often, and what they do when it fails.
Follow-up
- What breaks first when traffic grows ten times?
- What would you drop to keep the system up under load?
How would you design a high-throughput, low-latency Identity Provider …
How would you design a high-throughput, low-latency Identity Provider (IdP) system?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- 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
- How does this behave when that dependency is down for an hour?
- What would you drop to keep the system up under load?
A resumable usage export that never skips a row
Customers pull their own rows from usage_event through GET /v1/usage to reconcile against their own systems. The table is append-only and partitioned daily on ingested_at; a large tenant adds millions of rows a day while the export is being walked, and a client may pause for hours and resume. Specify the cursor, the index it requires, the tenant scoping, the ordering guarantee you can honestly offer, the per-page cost as the walk deepens, and what the client must do to avoid missing rows.
Approach
- Rule out LIMIT/OFFSET on two independent grounds and say both, because fixing only one leaves the other. Correctness: rows inserted between page requests shift the window, so a walking client skips rows and repeats others, which for a reconciliation consumer is silent data loss rather than an error anyone sees. Cost: the database still produces and discards the skipped rows, so page N costs time proportional to N x page_size and a deep page degrades from milliseconds to seconds.
- Use keyset pagination over a stable, unique, indexed ordering: WHERE tenant_id = $1 AND (ingested_at, event_id) > ($2, $3) ORDER BY ingested_at, event_id LIMIT $4, carrying the last row's pair as the cursor. The row-value comparison navigates a composite btree directly, so each page is O(log n + page_size) and stays constant as the walk deepens. The precondition is that the cursor columns never change value for a row, which ingested_at satisfies and updated_at would not.
- Lead the index with tenant_id - (tenant_id, ingested_at, event_id) - which is simultaneously the correctness guard and the plan choice. An index on (ingested_at) alone forces a filter across every tenant's rows, and on a table where one tenant holds most of them that is fine only for that tenant and terrible for everyone else. Because ingested_at is also the partition key, a resumed cursor prunes to the partitions from the cursor forward.
- Name the visibility hazard rather than assuming it away: in PostgreSQL now() is transaction start time, so a transaction that starts at T, inserts, and commits at T+8 s writes a row whose ingested_at is T but which becomes visible only at T+8 s. A walker that has already passed T never returns it. The gap equals the writer's longest transaction, so the mitigation is either a safety lag - serve only rows older than now() minus the longest permitted transaction - or a client that re-walks a trailing overlap window and deduplicates on event_id, which is stable and unique.
- Offer the guarantee you can actually keep: ordering by (ingested_at, event_id) with no claim whatsoever about occurred_at order, and completeness only behind the safety lag or with the documented overlap-and-dedup obligation on the client. Saying this in the API reference is part of the design, because the client's reconciliation logic is what has to absorb it.
Worked solution 20 min
- Write the keyset query and the exact index it needs, then read the query plan and confirm there is no Sort node.
- Insert rows concurrently while walking with a LIMIT/OFFSET pager and count the distinct rows returned against the rows that exist; repeat with the keyset pager and compare.
- Construct the out-of-order commit case by hand: open a transaction, insert, hold it open while the walker passes that timestamp, then commit, and check whether the walker ever returns that row.
- Choose the mitigation - safety lag or client overlap plus event_id dedup - and write down the number it depends on.
Follow-up
- The customer wants to reconcile by occurred_at instead of ingested_at. What breaks, and what would you offer them in its place?
- One tenant starts a full-history export. How do you keep it from occupying every connection in the pool?
- A customer reports a missing row. What do you check first, and what would each answer tell you?
One tenant's counter writes stall the whole connection pool
A change that made a per-tenant usage counter correct now produces site-wide latency whenever one large tenant writes: unrelated endpoints time out waiting for a connection while database CPU stays low and no statement is slow. The change wraps the counter update in a transaction that takes SELECT ... FOR UPDATE on one row, calls an external pricing service, then updates and commits. Give an ordered checklist, the arithmetic that bounds that tenant's write rate, and three repairs with the cost each one accepts.
Approach
- Separate waiting from working. Low database CPU alongside high application latency points at a queue, so instrument connection-acquisition wait separately from query execution time; that queue forms in the application and is invisible in database metrics, which is why the database looks healthy throughout.
- Confirm the lock rather than assuming it: sample waiting sessions and group by wait event, relation and tuple. Contention concentrated on one tuple belonging to one tenant is the signature; a deadlock would instead show the database aborting transactions after its detection timeout, which is not happening here.
- Do the arithmetic out loud. Throughput on a serialised row is one divided by the lock hold time, and the hold spans the external call, so a 20 ms pricing call caps that tenant near 50 writes per second no matter how many pods run. Every waiter also holds a pooled connection while it queues, so the shared pool drains and unrelated tenants fail at acquisition.
- Repair one: shrink the critical section to a single statement with the price resolved before the transaction opens. Cost is a stale price for the duration of one request and a second round trip; benefit is a hold time measured in the database's own execution time.
- Repairs two and three change where the contention lives rather than how long it is held. Sharding the counter into per-(tenant, bucket) rows and summing on read multiplies write throughput by the shard count, at the cost of an aggregate on every read and a shard count you must size against the largest tenant rather than the median. Accumulating in memory and flushing periodically removes the per-write round trip entirely, paid for with a bounded loss window on crash, which is acceptable for a rate limiter and not for a billing counter.
- Contain independently of which repair wins: a separate pool or per-tenant concurrency cap for this write class, a statement timeout low enough that a pathological query dies before it accumulates waiters, and an idle-in-transaction timeout so a stuck client cannot pin a connection and its locks.
Follow-up
- What would a genuine deadlock look like here, which two code paths would produce one, and how does the database's response differ from what you observed?
- If a transaction-pooling proxy sits in front of the database, which of your three repairs changes behaviour, and what stops working that would have worked on a direct connection?
- The counter also enforces a quota. Why is SELECT the count and then INSERT still wrong after you have fixed the contention?
Roughly ninety minutes on weeknights with one longer weekend block. The plan cuts scope rather than compressing everything, on the assumption that one thing finished per night beats four half-started.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and take a cold baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list and keep it visible all week.
- Take one twenty-five-minute coding problem and one fifteen-minute design prompt cold, and write the single sentence naming what blocked each, because those two sentences decide where the remaining evenings go.
- Set the week's rule: one thing finished every night, including the night you only have forty minutes.
Deliverable: A one-page scope with a not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Worked solution ↗02One pattern, written three times from blank
- Choose the single pattern most likely to appear in your loop and write it three times from an empty file rather than editing the previous attempt.
- On the third pass, write the invariant as a comment before the loop body and the complexity before the first line of code.
- Stop at ninety minutes even if the third version is imperfect, and write the one thing you would fix given another hour.
Deliverable: Three independent implementations of the same pattern plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗03One design, only to the depth you can defend
- Take one system shape and go only as far as requirements, interface and data model, refusing to draw a box you could not survive a follow-up about.
- Attach one number to each non-functional requirement, deriving it rather than asserting it, and write the assumption the number rests on.
- Write the one tradeoff you are choosing against and the observation that would make you reverse it.
Deliverable: One design at interface-and-schema depth with derived numbers and one written reversible tradeoff.
Practice prompt ↗Practice prompt ↗04Only the fundamentals you will have to defend
- Write, in under two hundred words each, the answers to the two questions that follow almost any implementation: why this structure and not the obvious alternative, and what happens to this code at a hundred times the input.
- Write what an index actually costs: faster lookups on the indexed columns against a write that now maintains a second structure, plus the cases where the planner declines to use it anyway, low selectivity, or a predicate wrapping the column in a function.
- Delete any answer you cannot deliver aloud in under a minute, since an answer that needs reading is not an answer you have.
Deliverable: Three written answers, each under two hundred words and each timed aloud.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a ninety-second and a four-minute version of your main project and time both aloud rather than reading them.
- Prepare the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be ready to say exactly where it came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in the weekend block
- Run a sixty-minute mock covering a coding round and a design round in one sitting with no break, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the twenty-minute warm-up you will actually do on the morning: one problem you can already solve from a blank file, one design you can narrate, and nothing you have never seen.
- Re-read only your own notes from this week and open no new material.
- Write the logistics down: the editor or shared document you will be working in, whether execution and lookups are permitted, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the design structure, the project numbers, and the logistics.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Counting review comments or mentees proves nothing. The useful version is a specific change you approved with a reservation you stated, or one you blocked and the delay that cost. Say which standard you were holding and why it was worth the friction. A mentoring story needs the thing the other person can now do without you.
Describe a situation where you had to quickly pick up a new technology…
Describe a situation where you had to quickly pick up a new technology stack or framework to deliver a feature.
Approach
- Name the disagreement and how you resolved it with evidence.
- Pick a story where you made the decision, not one where you watched it.
- Give the blast radius: what could have broken, and what you measured.
Follow-up
- How did you know your change caused the improvement?
- What would you do differently if you ran that again?
How do you handle a situation where you disagree with a senior enginee…
How do you handle a situation where you disagree with a senior engineer or architect on a design decision?
Approach
- Close with what you would do differently, concretely.
- Name the disagreement and how you resolved it with evidence.
- Pick a story where you made the decision, not one where you watched it.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Estimate a tenant-leading index migration you have never run
Someone needs a date. usage_event carries an index on (occurred_at) and needs (tenant_id, occurred_at); the largest tenant holds roughly a hundred times the median tenant's rows, the table is partitioned daily with years of retention, and you have never run a migration on a table this large. Give an estimate you would defend: how you decompose the work, the two or three numbers you would go and measure first, the range and confidence you state, and what you commit to when the person asking needs a single date today.
Approach
- Refuse the bare number and then give one anyway, in the form that is actually useful: a range plus the measurement that collapses it. 'Four to eleven days; one afternoon building this index on a restored copy of the largest partition takes that to within a day' is an answer, while 'it depends' is not.
- Decompose by failure mode rather than into equal chunks, because that is where estimates go wrong. On a partitioned parent you create the index ON ONLY the parent, build each partition's index with CREATE INDEX CONCURRENTLY, then ALTER INDEX ... ATTACH PARTITION, at which point the parent index becomes valid. CONCURRENTLY does not block writes but scans each partition twice, waits out older transactions, cannot run inside a transaction block, and on failure leaves an invalid index you must drop concurrently and retry.
- Name the two unknowns that dominate and price them: build time on one restored partition of realistic size, and whether the planner actually chooses the new index for the skewed tenant, since selectivity for a tenant holding most of the rows is a different question from selectivity for the median tenant. Both are half-day measurements against a replica, and both are cheaper than being wrong by a week.
- State the assumptions the range is conditional on, because that is what makes a slip a re-estimate instead of a credibility event: no partition above a stated row count, one concurrent build at a time so it does not compete with ingest for I/O, and an ingest backlog that can absorb the added write amplification while both indexes exist.
- Budget the step nobody budgets: verification and the old index's removal. Dropping the old index is fast, but deciding it is safe to drop means confirming no plan still uses it, and that confirmation waits on real traffic across a full weekly cycle rather than on your patience.
- Answer the single-date request honestly. Commit to a date for the first checkpoint — the measured build number from the replica — and to re-estimating on that date, and say plainly what you are not committing to yet. A date with a scheduled re-estimate is worth more to the asker than a confident wrong one, and you should say why in those words.
Follow-up
- The concurrent build fails half way through the largest partition. What is the state of the database and what do you do next?
- Your estimate slips by sixty percent. Which assumption broke, and at what point would you have known?
- The person asking needs the date for a customer commitment. Does your answer change?
- 01
Describe a situation where you had to quickly pick up a new technology stack or framework to deliver a feature.
- 02
How do you handle a situation where you disagree with a senior engineer or architect on a design decision?
- 03
Someone needs a date. usage_event carries an index on (occurred_at) and needs (tenant_id, occurred_at); the largest tenant holds roughly a hundred times the median tenant's rows, the table is partitioned daily with years of retention, and you have never run a migration on a table this large. Give an estimate you would defend: how you decompose the work, the two or three numbers you would go and measure first, the range and confidence you state, and what you commit to when the person asking needs a single date today.
Is this an official Ping Identity interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Ping Identity. Rounds and questions reflect what candidates have reported, not a process Ping Identity has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the Ping Identity interview process?
The process is moderately difficult and highly structured. The technical interviews focus on core fundamentals rather than obscure brainteasers, but the mandatory CCAT cognitive test and the deep-dive review of your take-home assignment add significant rigor to the loop.
PracHub interview research ↗What is the most common reason candidates do not pass the interview loop?
Many candidates struggle with the strict time limits of the CCAT cognitive test, while others fail to deliver a sufficiently polished, production-grade solution for the take-home assignment. Treating the take-home as a casual script rather than a professional deliverable is a frequent pitfall.
PracHub interview research ↗How much does Ping Identity value domain-specific security experience?
While having a background in IAM, OAuth, or SAML is highly beneficial, it is not a strict prerequisite. Ping Identity values strong software engineering fundamentals, systems thinking, and clean coding practices above all else, trusting that talented engineers can learn the security domain on the job.
PracHub interview research ↗What is the typical timeline from the first recruiter call to an offer?
The entire process generally takes between 3 to 4 weeks. Ping Identity's recruiting team is typically very communicative and moves candidates through the stages efficiently once the initial cognitive assessments are completed.
PracHub interview research ↗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.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-24 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-24 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-24
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