Timeline
- Round 1 (Coding): March 20
- Round 2 (HM): March 24
- Round 3 (Real-world problem): March 26
- Round 4 (HLD): March 27
- Offer call: March 31
- Offer letter: April 2
- Total: ~2 weeks end-to-end
Round 1 — Coding (60 min)
Problem: Design a versioned datastore with PUT and GET operations.
- PUT(docId, contents, timestamp) — stores versioned content
- GET(docId, timestamp) — returns content at or just before the given timestamp
- PUTs can arrive out of timestamp order
My approach: HashMap mapping docId to a BST of (timestamp → content). GET becomes a floor query on the BST. Since Go doesn't have a native self-balancing BST, I implemented it with a basic BST.
Ran through test cases — worked correctly. Discussed trade-offs (sorted array + binary search vs BST vs skip list).
Result: Moved to next round.
Round 2 — Hiring Manager (60 min)
This went extremely well. The HM was genuinely interested in a project I'd built at my current company. He actually explored the feature live during the interview.
The technical deep dive covered:
- Architecture and design decisions
- How I handled edge cases in production
- Performance characteristics and scaling considerations
- Team structure and my role/ownership
Followed by standard behavioral:
- Why MongoDB?
- How I handle disagreements in code reviews
- A time I delivered under tight constraints
Key takeaway: Having a project you can demo live and talk about for 30+ minutes with genuine depth is incredibly powerful. This round basically carried my candidacy.
Round 3 — Real-world Problem (60 min)
Interviewer was based in Canada. The problem was domain-relevant (data migration team):
Problem: Design a verifier process for data migration. Monitor replication lag, alert if data hasn't appeared in the secondary datastore within K seconds.
I proposed a solution with a HashMap tracking in-flight items + periodic sweep for alerting. Discussed event ordering challenges and scalability.
Self-assessment: Lean hire. I felt I didn't go deep enough on the edge cases (out-of-order events, verifier crash recovery). The interviewer had to prompt me on a couple of things I should've proactively addressed.
Round 4 — High-Level Design (60 min)
Interviewer was based in Sydney, super friendly.
Problem: Design a database migration platform for a company like TicketMaster transitioning from relational to NoSQL.
I asked a lot of clarifying questions:
- Scale of data (TBs? PBs?)
- Acceptable downtime / migration strategy (big bang vs incremental?)
- Consistency requirements during transition
- Rollback strategy
Covered:
- Dual-write vs CDC (Change Data Capture) approaches
- Schema mapping layer (relational → document model)
- Validation and reconciliation
- Cutover strategy with feature flags
- Monitoring and alerting during migration
Interviewer seemed happy with the breadth and depth.
The Offer
Recruiter called on March 31:
- Round 1, 2, 4: positive feedback
- Round 3: not positive
- But the excellent HM round feedback tipped the decision in my favor
Offer letter came on April 2.
Tips for MongoDB SWE-3
- Have a killer project story — one you can talk about for 30 minutes with genuine technical depth. This can carry your entire candidacy if the HM loves it.
- Coding round is practical, not LeetCode — they ask real-world data structure design problems, not algorithm puzzles. Think: build a system component.
- Domain awareness helps — I was interviewing for the data migration team. Knowing basics of CDC, replication, eventual consistency helped in Rounds 3 and 4.
- Ask clarifying questions in HLD — the interviewer noted this positively. Don't dive into solutioning before understanding scope.
- One weak round doesn't kill you — if other rounds are strong enough (especially HM), they'll still roll out the offer.
- Go's lack of TreeMap/sorted structures — be prepared to implement or discuss alternatives if you're coding in Go.