As a Software Engineer at Obsidian Security, you will play a critical role in defining the future of SaaS Security Posture Management (SSPM). Obsidian Security is dedicated to protecting SaaS environments from complex threats, insider risks, and posture misconfigurations. In this role, you will design and build highly scalable, fault-tolerant systems capable of processing massive volumes of event data and configurations from various SaaS platforms like Microsoft 365, Salesforce, and Workday. Your engineering efforts will directly impact how enterprise organizations detect and mitigate security threats in real-time. You will work on sophisticated data pipelines, API integrations, and security analysis frameworks that must operate seamlessly at immense scale. This requires a deep understanding of distributed systems, cloud infrastructure, and modern security patterns. The engineering organization at Obsidian Security values practical problem-solving, real-world thinking, and collaborative execution. You will face complex challenges involving high-throughput data ingestion, complex event processing, and security analysis. For engineers who thrive on solving hard technical problems at the intersection of cloud scale and cybersecurity, this position offers an incredibly rewarding and high-impact environment.
HR Screen
reportedDiscuss your background, career goals, and compensation expectations.
What to demonstrate
- Discuss your background, career goals, and compensation expectations
- Depth in Systems Design
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 Deep Dive
reportedEngage in a technical deep dive with a hiring manager.
What to demonstrate
- Engage in a technical deep dive with a hiring manager
- Depth in Systems Design
How to prepare
- Answer aloud and timed: Design a system to detect anomalous user behavior across multiple independent SaaS platforms in near real-time.
- Answer aloud and timed: How do you ensure data isolation and security in a multi-tenant SaaS architecture?
Online Technical Assessment
reportedComplete a tricky online technical assessment.
What to demonstrate
- Complete a tricky online technical assessment
- Depth in Systems Design
How to prepare
- Answer aloud and timed: Walk me through the architecture of a distributed system that requires transactional consistency across multiple microservices.
- Answer aloud and timed: Given a stream of user activity logs, write an efficient algorithm to identify potential session-hijacking patterns.
Panel Phase
reportedParticipate in a rigorous panel that tests system design and security analysis skills.
What to demonstrate
- Participate in a rigorous panel that tests system design and security analysis skills
- Depth in Systems Design
How to prepare
- Answer aloud and timed: Implement a thread-safe rate limiter that can be distributed across multiple application nodes.
- Answer aloud and timed: How would you optimize a slow-running database query that aggregates millions of security event records?
Executive Interview
reportedSome candidates will face an executive interview focusing on background and career decisions.
What to demonstrate
- Some candidates will face an executive interview focusing on background and career decisions
- Depth in Systems Design
How to prepare
- Answer aloud and timed: Solve a complex data parsing and transformation problem designed to test your handling of malformed JSON payloads.
- Answer aloud and timed: Tell me about a time you had to make a significant technical trade-off under a tight deadline. What was the outcome?
Final Decision
reportedWait for the final decision, which may take up to two weeks after the panel.
What to demonstrate
- Wait for the final decision, which may take up to two weeks after the panel
- Depth in Systems Design
How to prepare
- Answer aloud and timed: Describe a situation where you had to defend a controversial technical design decision to senior leadership.
- Answer aloud and timed: Why did you choose to work at your previous companies, and how did those experiences shape your engineering philosophy?
PracHub editorial advice for the preparation topics above.
Structure your system design answers
Use a structured framework when answering design questions. Start by defining requirements and scale, move to high-level architecture, and then dive deep into specific components like data flow, storage, and security.
Be ready to defend your resume
Review your past projects thoroughly. Be prepared to explain why you chose specific technologies, what the trade-offs were, and what you would do differently today.
Focus on practical security
Since Obsidian Security is a cybersecurity company, always keep security in mind during your design and coding interviews. Mention data encryption, secure communication protocols, and access control where appropriate.
If you are asked to travel for on-site interviews, keep detailed records of your travel expenses
Obsidian Security is diligent about reimbursing candidates for approved interview-related travel costs.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Given a stream of user activity logs, write an efficient algorithm to identify potential session-hijacking pat
Given a stream of user activity logs, write an efficient algorithm to identify potential session-hijacking patterns.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Solve a complex data parsing and transformation problem designed to test your handling of malformed JSON paylo
Solve a complex data parsing and transformation problem designed to test your handling of malformed JSON payloads.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
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?
How would you optimize a slow-running database query that aggregates millions of security event records?
How would you optimize a slow-running database query that aggregates millions of security event records?
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?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
How would you design a scalable data ingestion pipeline to process high-volume event logs from multiple third-
How would you design a scalable data ingestion pipeline to process high-volume event logs from multiple third-party SaaS APIs?
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?
Explain how you would handle rate limiting and API throttling when fetching data from external SaaS providers.
Explain how you would handle rate limiting and API throttling when fetching data from external SaaS providers.
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?
Design a system to detect anomalous user behavior across multiple independent SaaS platforms in near real-time
Design a system to detect anomalous user behavior across multiple independent SaaS platforms in near real-time.
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 ensure data isolation and security in a multi-tenant SaaS architecture?
How do you ensure data isolation and security in a multi-tenant SaaS architecture?
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?
Walk me through the architecture of a distributed system that requires transactional consistency across multip
Walk me through the architecture of a distributed system that requires transactional consistency across multiple microservices.
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?
Implement a thread-safe rate limiter that can be distributed across multiple application nodes.
Implement a thread-safe rate limiter that can be distributed across multiple application nodes.
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?
p99 jumped on one listing filter while p50 stayed flat
After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.
Approach
- Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
- Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
- Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
- Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.
Follow-up
- The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
- How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?
Built from the rounds and topics Obsidian Security candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Obsidian Security loop
- Write out the reported sequence: HR Screen, Technical Deep Dive, Online Technical Assessment, Panel Phase, Executive Interview, Final Decision.
- 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 6 reported rounds, with the weakest marked.
02Work Systems Design
- Spend the session on Systems Design, which Obsidian Security candidates report being tested on.
- Write one worked example in Systems Design and time yourself on it.
Deliverable: One timed worked example in Systems Design.
03Work Security Analysis
- Spend the session on Security Analysis, which Obsidian Security candidates report being tested on.
- Write one worked example in Security Analysis and time yourself on it.
Deliverable: One timed worked example in Security Analysis.
04Work System and Security Analysis (Holistic Thinking)
- Spend the session on System and Security Analysis (Holistic Thinking), which Obsidian Security candidates report being tested on.
- Write one worked example in System and Security Analysis (Holistic Thinking) and time yourself on it.
Deliverable: One timed worked example in System and Security Analysis (Holistic Thinking).
05Answer out loud: System Design and Security Analysis
- Answer aloud, timed: How would you design a scalable data ingestion pipeline to process high-volume event logs from multiple third-party SaaS APIs?
- Answer aloud, timed: Explain how you would handle rate limiting and API throttling when fetching data from external SaaS providers.
Deliverable: Spoken answers to 2 reported System Design and Security Analysis question(s), under time.
06Answer out loud: Coding and Practical Problem-Solving
- Answer aloud, timed: Given a stream of user activity logs, write an efficient algorithm to identify potential session-hijacking patterns.
- Answer aloud, timed: Implement a thread-safe rate limiter that can be distributed across multiple application nodes.
Deliverable: Spoken answers to 2 reported Coding and Practical Problem-Solving question(s), under time.
07Answer out loud: Behavioral and Experience
- Answer aloud, timed: Tell me about a time you had to make a significant technical trade-off under a tight deadline. What was the outcome?
- Answer aloud, timed: Describe a situation where you had to defend a controversial technical design decision to senior leadership.
Deliverable: Spoken answers to 2 reported Behavioral and Experience 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.
Tell me about a time you had to make a significant technical trade-off under a tight deadline. What was the ou
Tell me about a time you had to make a significant technical trade-off under a tight deadline. What was the outcome?
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?
Describe a situation where you had to defend a controversial technical design decision to senior leadership.
Describe a situation where you had to defend a controversial technical design decision to senior leadership.
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?
Why did you choose to work at your previous companies, and how did those experiences shape your engineering ph
Why did you choose to work at your previous companies, and how did those experiences shape your engineering philosophy?
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 feedback when an interviewer or peer is highly critical of your past project architecture?
How do you handle feedback when an interviewer or peer is highly critical of your past project architecture?
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
Tell me about a time you had to make a significant technical trade-off under a tight deadline. What was the outcome?
- 02
Describe a situation where you had to defend a controversial technical design decision to senior leadership.
- 03
Why did you choose to work at your previous companies, and how did those experiences shape your engineering philosophy?
- 04
How do you handle feedback when an interviewer or peer is highly critical of your past project architecture?
How difficult is the interview process for a Software Engineer at Obsidian Security?
The process is generally considered difficult to very difficult. It features a tricky online assessment and a comprehensive panel round that thoroughly tests your system design and security knowledge. Successful candidates typically spend significant time preparing for distributed systems design and security concepts.
Obsidian Security Software Engineer candidate reports ↗What is the engineering culture like at Obsidian Security?
The culture is highly collaborative, sharp, and focused on solving real-world security challenges. The team values practical, working solutions over theoretical complexity. They appreciate engineers who are invested in security, continuous learning, and high-quality engineering standards.
Obsidian Security Software Engineer candidate reports ↗How should I prepare for the executive interview round?
Be ready for a highly direct and potentially critical review of your resume and past employers. Focus on explaining the impact of your work, the reasoning behind your career moves, and how you handle professional challenges. Maintain a confident, professional demeanor throughout the discussion.
Obsidian Security Software Engineer candidate reports ↗Does Obsidian Security offer remote work opportunities?
Yes, Obsidian Security offers remote roles, particularly for senior and specialized engineering positions, alongside hybrid opportunities in their key office locations such as Newport Beach, CA, and Palo Alto, CA.
Obsidian Security Software Engineer candidate reports ↗What topics does Obsidian Security test in interviews?
Obsidian Security interviews most often cover Site Reliability Engineering (SRE), Stakeholder Communication, Monitoring & Observability, Security Domain Knowledge, and Python. The exact emphasis depends on the specific role you apply for.
Obsidian Security 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.
- 01Obsidian Security 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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