Interview Prep — Apache Committer Track & Staff/Principal Data Infrastructure
This section prepares you for two distinct evaluation processes:
- The committer "interview" that isn't an interview. Apache committership is granted by PMC vote based on your public track record — every JIRA comment, patch, review, and mailing-list thread is the interview. The governance Q&A here makes sure you understand the system you are being evaluated by.
- Staff/Principal interviews at data-infrastructure companies (Databricks, Confluent, Cloudera, LinkedIn, Uber, Netflix, Stripe…), where Apache-ecosystem depth, JVM performance fluency, distributed-systems design, and review judgment are the bar.
Contents
| File | Covers |
|---|---|
| 01-apache-governance-questions.md | ASF structure, releases, votes, vetoes, committer/PMC process — with complete answers |
| 02-distributed-systems-questions.md | Consensus, replication, partitioning, scheduling, fault tolerance — with complete answers |
| 03-jvm-performance-questions.md | GC, JIT, memory model, concurrency, benchmarking — with complete answers |
| 04-code-review-scenarios.md | Review exercises with model reviewer responses |
| 05-behavioral-star-stories.md | STAR frameworks for staff-level behavioral rounds, OSS-flavored |
How to use this section
- After Phases 01–04: work through file 01. You should be able to answer every question without looking, in the words a PMC member would use.
- After Phases 07–08: files 02 and 03. These map directly to the labs — every answer references the lab where you built the thing being asked about.
- After Phases 05–06, 11: file 04. Review judgment is the single most-weighted signal for committer candidacy and for staff interviews alike.
- Two weeks before any interview loop: file 05, written in your own words from your own labs.
The meta-skill
In every answer, demonstrate the same three habits the curriculum trains:
- Mechanism before opinion — explain how the thing works before judging it.
- Compatibility and blast radius — every change is discussed in terms of who it breaks.
- Evidence — benchmarks, tests, and references to real systems (Spark, Hadoop, Hive, Kafka) instead of generalities.