Chubb Data Engineer Interview Questions (2026)

**Section 1 — The Context (The 'Why')** Medallion addresses tension between raw preservation (audit) and curated analytics. Bronze retains everything; Silver/Gold add structure. **Section 2 — The Diagram** ``` [Sources] --> [Bronze] --> [Silver] --> [Gold] Append Dedup/Merge Marts ``` **Section 3 — Component Logic** **Bronze** raw append-only, immutable. **Silver** schema, dedup, Delta MERGE. **Gold** star schemas. **TTL policies** move Bronze to Glacier. **Partitioning**: date, source. **Data skew mitigation** in Silver: salting. **Fan-out**: one Gold serves BI, ML, API. **Exactly-once** at each layer via merge keys. **Idempotency** via dedup keys. Why Bronze: audit, replay. Why Silver: clean. Why Gold: business agg. **Section 4 — The Trade-offs (The 'Senior' part)** - **CAP Theorem**: AP: Bronze eventual; Silver/Gold atomic per table. Minutes staleness OK for analytics. - **Cost vs. Performance**: Bronze S3 $0.023/GB. Glacier after 90d. Silver/Gold: merge compute. ~$0.10/TB end-to-end. - **Blast Radius**: Bronze fail: replay. Silver fail: retry; merge idempotent. Gold fail: dashboards stale. Isolated; no cascade. **Section 5 — Pro-Tip** **Pro-Move**: 'Bronze 90d hot, 1yr Glacier; Silver/Gold 2yr.' **Red Flag**: Logic in Bronze. **Supplemental (Senior Context)**: In production, monitor partition skew, consumer lag, and merge duration. Use correlation IDs for traceability across pipeline stages. Schema evolution: prefer additive changes only; use Schema Registry for streaming to enforce compatibility. Consider data contract tests in CI to catch breaking changes early. Budget 10-20% overhead for replication, checkpoint storage, and DLQ. Data quality gates at each layer prevent bad data propagation. Right-size resources: profile before scaling; over-provisioning wastes budget. Document runbooks for common failures: broker restart, consumer rebalance, sink timeout. Establish SLOs per stage: ingest latency, transform duration, serve freshness. Review partition key choice: avoid high-cardinality keys that cause explosion; use composite keys (date, tenant) for balanced distribution. Test failure injection: kill executors, broker, sink to validate recovery. Optimize for the common case: most queries filter by date. Cold start mitigation: pre-warm connections, cache dimension lookups. Alert on lag exceeding 1hr, error rate above 1%. Cost optimization: lifecycle policies, spot instances, partition pruning. Lineage tracking enables impact analysis. Idempotency keys for replay. Backpressure handling prevents slow consumers from blocking producers. Fan-out patterns allow multiple consumers without re-processing. Exactly-once semantics require replayable source and idempotent sink. Data skew mitigation via salting for high-cardinality joins. Partitioning strategies must align with query patterns for pruning. CAP trade-off: AP for ingest and transform; CP for serve when BI needs accuracy. Blast radius bounded by partition and consumer group. Measure and iterate: latency percentiles, cost per record, error rate. Principal engineer tip: quantify before and after optimizations. Red flag: describing architecture without trade-offs. Glue versus EMR: Glue for bursty sub-2hr jobs; EMR for sustained 8hr+ saving 60%. MSK for Kafka; S3 for lake storage. Self-heal: orchestration retries; idempotent sinks ensure consistency. If primary fails, downstream goes stale but no data loss with replay. Design for operability: runbooks, dashboards, alerts. Avoid tight coupling between stages. Incremental processing reduces compute versus full refresh. Watermark-based deduplication enables idempotency. Partition evolution: add new partitions without rewriting. Retention policies balance cost and compliance. Test at scale: use production-size samples for validation. Always document trade-offs.