As a Software Engineer at Raft (VA), you will play a crucial role in designing and developing scalable, data-intensive backend systems that power flagship products for demanding federal and public agency clients. Operating at the intersection of distributed data systems, cloud-native platforms, and complex application development, you will build digital solutions that directly impact mission-critical operations and the lives of millions of Americans. Your work focuses heavily on modernizing public-sector technology using cutting-edge tech stacks and customer-obsessed design thinking. You will drive the technical execution by architecting robust backend solutions using Golang and/or Java, ensuring top-tier security, performance, and reliability across systems. Beyond writing production-grade code, you act as a hands-on technical leader who mentors junior and mid-level engineers while collaborating directly with users and customers at deployment sites. This involves gathering operational feedback, refining real-world performance, and ensuring that modular, extensible architectures can evolve rapidly to meet changing mission needs. This role requires a unique blend of deep technical expertise and a highly collaborative, mission-driven mindset. You will thrive in fast-paced environments where navigating ambiguity, working within cloud-native and containerized ecosystems, and maintaining secure development practices are everyday expectations.
Initial Screening
reportedThe process begins with an initial screening to assess candidate qualifications.
What to demonstrate
- The process begins with an initial screening to assess candidate qualifications
- Depth in Golang (Go)
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Assessments
reportedCandidates undergo technical assessments that include coding challenges and design discussions.
What to demonstrate
- Candidates undergo technical assessments that include coding challenges and design discussions
- Depth in Golang (Go)
How to prepare
- Answer aloud and timed: How do you approach building and scaling data-intensive systems and distributed architectures?
- Answer aloud and timed: What experience do you have with data processing frameworks, streaming technologies, and real-data pipelines?
Final Interviews
reportedFinal interviews consist of behavioral questions and discussions about cultural fit.
What to demonstrate
- Final interviews consist of behavioral questions and discussions about cultural fit
- Depth in Golang (Go)
How to prepare
- Answer aloud and timed: How do you leverage Agentic AI coding tools like Claude Code, and what do you see as their primary limitations?
- Answer aloud and timed: Interviewers use this area to test your ability to structure modular, extensible systems that remain resilient under complex operational constraints.
PracHub editorial advice for the preparation topics above.
Adopt a product mindset
Approach every technical question and system design scenario as if you are building a product for a real user, keeping mission impact front and center.
Highlight hands-on coding
Be prepared to discuss specific architectural patterns you have coded yourself, as interviewers value engineers who remain actively involved in writing production code.
Communicate your trade-offs
When answering system design questions, explicitly state your assumptions and discuss why you chose specific technologies like Golang, Java, or particular Kubernetes operators.
Embrace the Ubuntu philosophy
Showcase times when you elevated your peers, mentored junior developers, or contributed to a collaborative team culture during your behavioral discussions.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
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?
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?
Canonicalise a request body into a stable idempotency fingerprint
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?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
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?
This category evaluates your core programming proficiency, backend fundamentals, and familiarity with the spec
This category evaluates your core programming proficiency, backend fundamentals, and familiarity with the specific technology stack used across products.
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 is your familiarity with Golang and Java in high-throughput backend environments?
What is your familiarity with Golang and Java in high-throughput backend environments?
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 building and scaling data-intensive systems and distributed architectures?
How do you approach building and scaling data-intensive systems and distributed architectures?
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 experience do you have with data processing frameworks, streaming technologies, and real-data pipelines?
What experience do you have with data processing frameworks, streaming technologies, and real-data pipelines?
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 leverage Agentic AI coding tools like Claude Code, and what do you see as their primary limitations
How do you leverage Agentic AI coding tools like Claude Code, and what do you see as their primary limitations?
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?
Interviewers use this area to test your ability to structure modular, extensible systems that remain resilient
Interviewers use this area to test your ability to structure modular, extensible systems that remain resilient under complex operational constraints.
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 would you design a scalable microservices architecture for an air-gapped or disconnected environment?
How would you design a scalable microservices architecture for an air-gapped or disconnected environment?
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 strategies do you use to ensure high performance and reliability across mission-critical applications?
What strategies do you use to ensure high performance and reliability across mission-critical applications?
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 container orchestration, including creating and managing Helm charts and Kubernetes operat
How do you approach container orchestration, including creating and managing Helm charts and Kubernetes operators?
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?
Can you explain how you would handle ICAM and security best practices within cloud-native systems?
Can you explain how you would handle ICAM and security best practices within cloud-native systems?
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 Raft (VA) candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Raft (VA) loop
- Write out the reported sequence: Initial Screening, Technical Assessments, Final Interviews.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.
02Work Golang (Go)
- Spend the session on Golang (Go), which Raft (VA) candidates report being tested on.
- Write one worked example in Golang (Go) and time yourself on it.
Deliverable: One timed worked example in Golang (Go).
03Work Backend Software Engineering
- Spend the session on Backend Software Engineering, which Raft (VA) candidates report being tested on.
- Write one worked example in Backend Software Engineering and time yourself on it.
Deliverable: One timed worked example in Backend Software Engineering.
04Work Distributed Systems
- Spend the session on Distributed Systems, which Raft (VA) candidates report being tested on.
- Write one worked example in Distributed Systems and time yourself on it.
Deliverable: One timed worked example in Distributed Systems.
05Answer out loud: Technical and Backend Experience
- Answer aloud, timed: This category evaluates your core programming proficiency, backend fundamentals, and familiarity with the specific technology stack used across products.
- Answer aloud, timed: What is your familiarity with Golang and Java in high-throughput backend environments?
Deliverable: Spoken answers to 2 reported Technical and Backend Experience question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: Interviewers use this area to test your ability to structure modular, extensible systems that remain resilient under complex operational constraints.
- Answer aloud, timed: How would you design a scalable microservices architecture for an air-gapped or disconnected environment?
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and Cultural Fit
- Answer aloud, timed: These questions assess how you collaborate with teammates, handle technical leadership, and align with the core philosophy of the organization.
- Answer aloud, timed: How do you approach mentoring junior and mid-level engineers while balancing your own coding deliverables?
Deliverable: Spoken answers to 2 reported Behavioral and Cultural Fit 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.
These questions assess how you collaborate with teammates, handle technical leadership, and align with the cor
These questions assess how you collaborate with teammates, handle technical leadership, and align with the core philosophy of the organization.
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?
How do you approach mentoring junior and mid-level engineers while balancing your own coding deliverables?
How do you approach mentoring junior and mid-level engineers while balancing your own coding deliverables?
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?
Tell me about a time you worked directly with a customer or stakeholder in an operational environment to gathe
Tell me about a time you worked directly with a customer or stakeholder in an operational environment to gather feedback.
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?
How do you handle competing technical priorities in a fast-paced environment?
How do you handle competing technical priorities in a fast-paced environment?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
What does customer obsession mean to you in the context of software delivery?
What does customer obsession mean to you in the context of software delivery?
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
These questions assess how you collaborate with teammates, handle technical leadership, and align with the core philosophy of the organization.
- 02
How do you approach mentoring junior and mid-level engineers while balancing your own coding deliverables?
- 03
Tell me about a time you worked directly with a customer or stakeholder in an operational environment to gather feedback.
- 04
How do you handle competing technical priorities in a fast-paced environment?
How difficult is the interview process, and how much preparation time should I plan?
The overall difficulty is generally rated as manageable to average, provided you have strong backend fundamentals and system design experience. Most candidates spend one to two weeks reviewing distributed systems patterns, container orchestration, and refreshing their Golang or Java skills before their loops.
Raft (VA) Software Engineer candidate reports ↗What differentiates successful candidates from those who are not offered a role?
Successful candidates stand out by demonstrating a balanced mix of deep technical competence and a genuine customer-obsessed mindset. They don't just talk about code architecture; they explain how their technical decisions directly benefit the end user in operational environments.
Raft (VA) Software Engineer candidate reports ↗What is the company culture and working style like for engineers?
The working style is highly collaborative, fast-paced, and guided by the core philosophy of Ubuntu and radical transparency. Engineers are treated as trusted technical leaders who own their products end-to-end, working closely with teammates and clients alike.
Raft (VA) Software Engineer candidate reports ↗What is the typical timeline from the initial recruiter screen to receiving an offer?
The process is notably streamlined and fast, often moving from initial contact to a final panel and offer within a week or two. Consistent communication from recruiters ensures that candidates are kept informed at every stage of the evaluation.
Raft (VA) Software Engineer candidate reports ↗Are there specific remote or onsite expectations for this position?
This role is based onsite at Hanscom AFB, MA, with foreign and domestic travel up to 25% required to visit customer sites and deployment locations. Candidates must be comfortable working directly within operational environments.
Raft (VA) Software Engineer candidate reports ↗What topics does Raft (VA) test in interviews?
Raft (VA) interviews most often cover Golang (Go), Backend Software Engineering, Distributed Systems, Scalable Architecture / Platforms at Scale, and Kubernetes. The exact emphasis depends on the specific role you apply for.
Raft (VA) 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.
- 01Raft (VA) 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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