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Nyc Department Of Buildings Software Engineer Interview Guide

Nyc Department Of Buildings Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at the Nyc Department Of Buildings, you are at the intersection of civic duty and modern technology. Your work directly impacts the safety and efficiency of New York City’s massive infrastructure by building and maintaining the digital systems that regulate construction, monitor building safety, and streamline city operations for millions of residents. This role is critical to the agency’s mission to modernize its technical footprint. You will contribute to high-impact projects that reduce bureaucracy and enhance data transparency. You can expect to work on complex, real-world problems that require a balance of technical rigor and a deep understanding of how government services affect the public. It is a unique opportunity to apply your engineering skills toward tangible, city-wide improvements.

This guide is scoped to a Software Engineer candidate at Nyc Department Of Buildings.

Focus areas

Behavioral Interviewing
Resume Mastery
Communication Skills

Where to focus your preparation

No round sequence has been reported for Nyc Department Of Buildings. Confirm the format with your recruiter.

Preparation focus

No round sequence has been reported for this company, so confirm the format with your recruiter and work the reported questions below.

What to demonstrate

Breadth across the topics this company reports testing
Whether you confirm the format before preparing for it

How to prepare

Ask the recruiter for the sequence, the duration of each stage and whether you will be writing code
Work the reported questions below and time yourself

Where candidates lose points

Take your time

If the interviewer is typing your answers, do not rush. Take a moment to formulate a structured, clear response.

Know your resume

Be prepared to dive deep into any project you list. You should be able to explain the architecture, your specific contribution, and the outcome.

Ask questions

Always prepare 3–5 thoughtful questions for the end of the interview. This is your chance to show that you have thought deeply about the role.

Be authentic

The team values honesty. If you don't know an answer, admit it, but explain how you would go about finding the solution.

Questions & practice

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
Where does the fingerprint get computed relative to request decompression and the body-size limit?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
How do you verify the backfill actually finished, given rows are still being inserted while it runs?

Convert the listing endpoint from offset pages to stable cursors

Medium · pagination · cursors · index access path

GET /v1/resources returns a tenant's resources newest-updated first, today with page and per_page, backed by index (tenant_id, status, updated_at DESC, resource_id DESC). Callers are a browser feed and a nightly sync job that walks every page. Users report items appearing twice or vanishing between pages, and page 400 is slow. Design the cursor contract: what the cursor contains and how it is encoded, the exact WHERE and ORDER BY, what happens when a row's updated_at changes mid-walk, how a client detects the end, and what the sync job does when a cursor is rejected.

Approach

Separate the two defects, because they have different fixes. Cost: OFFSET n makes the engine produce and discard n rows, so price grows with page depth rather than page size and page 400 pays for 400 pages of work. Correctness: while the set shifts, rows cross the offset boundary and are skipped or repeated, and nothing in the response lets the client detect it.
Write the seek: WHERE tenant_id = $1 AND status = $2 AND (updated_at, resource_id) < ($k, $id) ORDER BY updated_at DESC, resource_id DESC LIMIT n. The tie-break is not decoration - updated_at is not unique, and two rows sharing a timestamp across a page boundary reintroduce exactly the skip this was adopted to remove. PostgreSQL seeks the composite index on the row-value comparison directly; on an engine that does not optimise a row constructor, expand it into the equivalent OR form or the plan quietly degrades to a scan.
Encode the cursor as an opaque token carrying the sort key, the id, and a fingerprint of the filter and sort order, signed or at minimum validated. A cursor replayed against a different sort or filter must be a 400 with its own code, not a silently wrong page - the sync job cannot notice the difference otherwise.
State the guarantee precisely rather than generously. Keyset is stable against inserts and deletes elsewhere in the set, because the position is a value and not a count. It is not a snapshot: updated_at is mutable, so a row that is edited during the walk re-sorts and may be seen twice or not at all. If the sync job needs exactly-once coverage, order on an immutable key such as (created_at, resource_id), or walk resource_revision by revision_id and treat updated_at as data.

Follow-up

The client wants a total count and the ability to jump to page 400. What can you honestly offer instead, and what does each option cost?
How do you paginate backwards, and what does that require of the index?

Make resource creation safe for a client that retried a timeout

Medium · idempotency · retries · race conditions

POST /v1/resources creates a resource row and, in the same transaction, a resource_revision and an outbox_event. A partner's HTTP client times out at 2 seconds and retries twice; under load the first request is still executing when the retry arrives, and duplicate resources are appearing. Using idempotency_key (tenant_id, idempotency_key, request_fingerprint, state, response_status, response_body, resource_id, locked_until, expires_at), specify the whole contract: first call, concurrent retry, retry after completion, a key reused with a different body, and what the client does with each response.

Approach

Name why the plain REST shape fails here. POST is not idempotent, a timeout leaves the client unable to distinguish a lost request from a lost response, so it must retry - and the retry is a second create. Checking whether the key exists and then inserting it is check-then-act: under exactly the concurrency that caused the timeout, both attempts look, both see nothing, both proceed.
Let the unique constraint arbitrate instead. INSERT the key row with state 'in_flight' and locked_until set to now plus the request deadline, and commit that reservation on its own before any work is attempted - an uncommitted row is invisible to the concurrent retry, which is the entire point of writing it first. One caller wins the insert and proceeds; the other takes the unique violation and reads the row rather than falling through to the work.
Branch the loser on what it found. 'succeeded' replays response_status and response_body verbatim, with a header marking it a replay. 'in_flight' with locked_until in the future returns 409 and a Retry-After - do not park a request thread waiting on another request, that is how a concurrency bound is consumed by retries. 'in_flight' past locked_until is reclaimable by a conditional UPDATE predicated on the old locked_until, so exactly one reclaimer wins. A fingerprint mismatch is 422 and never the stored response: a different body under the same key is a client defect, and serving the old response hides it forever.
Commit the effect and the key's completion together - resource insert, revision row, outbox row, and the UPDATE of the reserved key to 'succeeded' with the stored response, in one transaction. That makes a crash binary: either all of it is durable and the key reads 'succeeded', or the transaction rolls back and the only durable trace is the reservation row, still 'in_flight', with no resource behind it. The reclaim branch finishes that case - once locked_until passes, one retry takes the row by conditional UPDATE and runs the work for the first time. Splitting the two commits is what creates the window with no recovery: the effect durable, the key still 'in_flight', and a reclaimer that cannot distinguish that from work never started, so it creates a second resource.

Follow-up

Response bodies are pruned at 24 hours but resource_id is kept. What do you return to a replay that arrives after pruning?
The endpoint now also sends a welcome email. Which part of this contract prevents a second send, and which part cannot?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the topics and questions Nyc Department Of Buildings candidates report; no round sequence has been reported.

Day 1 — Establish the Nyc Department Of Buildings format

No round sequence has been reported, so ask your recruiter for the sequence, the duration of each stage and whether you will write code.

Deliverable: A written reply from your recruiter confirming the format.

Day 2 — Work Behavioral Interviewing

Spend the session on Behavioral Interviewing, which Nyc Department Of Buildings candidates report being tested on.
Write one worked example in Behavioral Interviewing and time yourself on it.

Deliverable: One timed worked example in Behavioral Interviewing.

Day 3 — Work Resume Mastery

Spend the session on Resume Mastery, which Nyc Department Of Buildings candidates report being tested on.
Write one worked example in Resume Mastery and time yourself on it.

Deliverable: One timed worked example in Resume Mastery.

Day 4 — Work Communication Skills

Spend the session on Communication Skills, which Nyc Department Of Buildings candidates report being tested on.
Write one worked example in Communication Skills and time yourself on it.

Deliverable: One timed worked example in Communication Skills.

Day 5 — Answer out loud: Behavioral and Soft Skills

Answer aloud, timed: Tell me about yourself and your interest in the Nyc Department Of Buildings.
Answer aloud, timed: Describe a time you faced a difficult technical challenge and how you overcame it.

Deliverable: Spoken answers to 2 reported Behavioral and Soft Skills question(s), under time.

Day 6 — Rehearse your own examples

Prepare three examples from your own work where you made the decision, each with the outcome you can quantify.

Deliverable: Three examples written out, each with a number attached.

Day 7 — Dry run for Nyc Department Of Buildings

Run one full mock under time, then write down the two questions you most want to ask your interviewers.

Deliverable: A completed timed mock and two questions to ask.