As a Software Engineer at the Research Foundation of CUNY (RF CUNY), you are stepping into a role that bridges complex technical development with the expansive mission of public higher education and research. You will be building, maintaining, and scaling applications that directly support students, faculty, researchers, and administrative staff across the City University of New York network. Your work ensures that critical educational platforms, research administration tools, and internal systems operate seamlessly and efficiently. The impact of this position is deeply tied to the user experience of the academic community. Whether you are developing features for a Learning Management System (LMS) or architecting robust databases to handle complex research grants, your code facilitates the core operations of one of the largest public university systems in the United States. You will tackle challenges related to scale, data integrity, and cross-platform responsiveness, ensuring that end-users have reliable access to the resources they need. Expect a role that balances technical rigor with a strong sense of purpose. The environment is collaborative and highly professional, offering a unique opportunity to apply modern software engineering practices—such as advanced JavaScript frameworks and complex SQL database management—within an academic and research-driven context.
Resume Walkthrough
reportedInitial phase where interviewers dive deeply into your background, discussing specific projects and technical decisions.
What to demonstrate
- Initial phase where interviewers dive deeply into your background, discussing specific projects and technical decisions
- Depth in SQL
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.
Behavioral Screen
reportedAssessment of your past experiences and how they relate to the role, focusing on project ownership and collaboration.
What to demonstrate
- Assessment of your past experiences and how they relate to the role
- Focusing on project ownership and collaboration
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral screen above and write down what you would ask to confirm before it.
Technical Assessment
reportedTargeted questions regarding your technical stack, particularly focusing on JavaScript and advanced SQL queries.
What to demonstrate
- Targeted questions regarding your technical stack, particularly focusing on JavaScript and advanced SQL queries
- Depth in SQL
How to prepare
- Answer aloud and timed: Explain a complex technical decision you made to a non-technical stakeholder.
- Answer aloud and timed: Give me a specific example of how you ensured responsiveness in a recent web application.
Behavioral Evaluation
reportedDeeper behavioral evaluations to assess your problem-solving approach and communication skills.
What to demonstrate
- Deeper behavioral evaluations to assess your problem-solving approach and communication skills
- Depth in SQL
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral evaluation above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Master Your Resume
The interviewers will use your resume as the primary roadmap for the conversation. Be prepared to speak in-depth about every technology, project, and outcome you have listed. If it is on the page, it is fair game.
Prepare Concrete Examples
When asked about responsiveness or SQL, do not speak in generalities. Have specific stories ready: "In Project X, I used CSS Grid and media queries to solve Y problem," or "I optimized a query by adding an index to column Z."
Do not gloss over your past projects
Interviewers will ask for highly specific examples, such as how you implemented responsive design or optimized a slow SQL query in your previous roles.
Brush Up on Advanced SQL
Do not assume basic CRUD knowledge is enough. Review window functions, complex joins, subqueries, and performance optimization techniques. You need to prove you can handle complex data environments.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Archive a resource graph without breaking live references or recursing
Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.
Approach
- Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
- Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
- Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
- Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.
Follow-up
- The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
- The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
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?
Walk me through how you would optimize a query that is taking too long to execute.
Walk me through how you would optimize a query that is taking too long to execute.
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?
Denormalise tenant onto revisions and backfill it live
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?
Walk me through your resume and highlight the projects you are most proud of.
Walk me through your resume and highlight the projects you are most proud of.
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 discuss a time when a project did not go as planned and how you recovered?
Can you discuss a time when a project did not go as planned and how you recovered?
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?
Describe your role in the last major application you deployed.
Describe your role in the last major application you deployed.
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 balance technical debt with the need to release features quickly?
How do you balance technical debt with the need to release features quickly?
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?
Explain a complex technical decision you made to a non-technical stakeholder.
Explain a complex technical decision you made to a non-technical stakeholder.
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?
Give me a specific example of how you ensured responsiveness in a recent web application.
Give me a specific example of how you ensured responsiveness in a recent web 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?
What are the biggest challenges you face when developing for mobile versus desktop, and how do you solve them?
What are the biggest challenges you face when developing for mobile versus desktop, and how do you solve them?
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 JavaScript application?
How do you manage state in a complex JavaScript 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 your approach to optimizing front-end performance?
Can you explain your approach to optimizing front-end performance?
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 difference between a clustered and a non-clustered index?
Can you explain the difference between a clustered and a non-clustered index?
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?
Describe a scenario where you had to use complex joins and subqueries to extract specific reporting data.
Describe a scenario where you had to use complex joins and subqueries to extract specific reporting data.
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 ensure data integrity when performing large-scale updates?
How do you ensure data integrity when performing large-scale updates?
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 would you design a system to handle concurrent assignment submissions from thousands of students?
How would you design a system to handle concurrent assignment submissions from thousands of students?
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 key security considerations when building an application that handles student data?
What are the key security considerations when building an application that handles student data?
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?
How do you approach designing a scalable and maintainable REST API?
How do you approach designing a scalable and maintainable REST API?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
If you were tasked with rebuilding a legacy internal tool from scratch, where would you start?
If you were tasked with rebuilding a legacy internal tool from scratch, where would you start?
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?
Exports duplicate a row range about once a week
Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.
Approach
- Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
- Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
- Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
- Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.
Follow-up
- The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
- You now count takeovers. What alert fires on that counter, and at what threshold?
Built from the rounds and topics Research Foundation of CUNY candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Research Foundation of CUNY loop
- Write out the reported sequence: Resume Walkthrough, Behavioral Screen, Technical Assessment, Behavioral Evaluation.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.
02Work SQL
- Spend the session on SQL, which Research Foundation of CUNY candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
03Work Advanced SQL Queries
- Spend the session on Advanced SQL Queries, which Research Foundation of CUNY candidates report being tested on.
- Write one worked example in Advanced SQL Queries and time yourself on it.
Deliverable: One timed worked example in Advanced SQL Queries.
04Work Software Engineering (General)
- Spend the session on Software Engineering (General), which Research Foundation of CUNY candidates report being tested on.
- Write one worked example in Software Engineering (General) and time yourself on it.
Deliverable: One timed worked example in Software Engineering (General).
05Answer out loud: Resume and Experience Deep Dive
- Answer aloud, timed: Walk me through your resume and highlight the projects you are most proud of.
- Answer aloud, timed: Can you discuss a time when a project did not go as planned and how you recovered?
Deliverable: Spoken answers to 2 reported Resume and Experience Deep Dive question(s), under time.
06Answer out loud: Front-End and Responsive Design
- Answer aloud, timed: Give me a specific example of how you ensured responsiveness in a recent web application.
- Answer aloud, timed: What are the biggest challenges you face when developing for mobile versus desktop, and how do you solve them?
Deliverable: Spoken answers to 2 reported Front-End and Responsive Design question(s), under time.
07Answer out loud: Database and SQL Mastery
- Answer aloud, timed: What is your experience with writing advanced SQL queries?
- Answer aloud, timed: Can you explain the difference between a clustered and a non-clustered index?
Deliverable: Spoken answers to 2 reported Database and SQL Mastery 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.
Describe a time you had to debug a tricky UI inconsistency across different browsers.
Describe a time you had to debug a tricky UI inconsistency across different browsers.
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?
What is your experience with writing advanced SQL queries?
What is your experience with writing advanced SQL queries?
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?
Discuss your experience with developing or integrating a Learning Management System (LMS).
Discuss your experience with developing or integrating a Learning Management System (LMS).
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?
- 01
Describe a time you had to debug a tricky UI inconsistency across different browsers.
- 02
What is your experience with writing advanced SQL queries?
- 03
Discuss your experience with developing or integrating a Learning Management System (LMS).
How difficult is the interview process?
The difficulty is generally considered average compared to big tech, but it is highly thorough. The interviewers care less about trick algorithms and more about your practical ability to build responsive applications, write complex SQL, and articulate your past project experiences clearly.
Research Foundation of CUNY Software Engineer candidate reports ↗What is the culture like at Research Foundation of CUNY?
The culture is highly professional, respectful, and collaborative. Interviewers are known to be kind and understanding, treating candidates with respect regardless of their seniority. It is an environment that values learning, teamwork, and a shared mission to support CUNY’s educational goals.
Research Foundation of CUNY Software Engineer candidate reports ↗Do I need prior experience in higher education or with an LMS?
While having experience with a Learning Management System or working in EdTech is a distinct advantage and will be asked about, it is generally a "nice-to-have" rather than a strict requirement. Strong fundamental skills in JavaScript, SQL, and responsive design can outweigh a lack of domain experience.
Research Foundation of CUNY Software Engineer candidate reports ↗What is the typical timeline for the interview process?
The process usually moves at a standard pace, taking about 3 to 4 weeks from the initial recruiter screen to a final decision. You will typically have a few days to prepare between the initial resume walkthrough and the deeper technical rounds.
Research Foundation of CUNY Software Engineer candidate reports ↗Are there opportunities for mentorship and growth?
Yes. Candidates have noted that the environment provides excellent opportunities to work on technical skills and learn how to collaborate effectively with diverse teams. The supportive nature of the staff makes it a strong place for steady career growth.
Research Foundation of CUNY Software Engineer candidate reports ↗What topics does Research Foundation of CUNY test in interviews?
Research Foundation of CUNY interviews most often cover Statistical Analysis, Data Analysis, Stakeholder Communication, SQL, and Requirements Gathering. The exact emphasis depends on the specific role you apply for.
Research Foundation of CUNY 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.
- 01Research Foundation of CUNY 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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