Data Engineer
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- Master the interview loop and data-model fundamentals
- Design reliable batch and streaming pipelines
- Defend platform reliability, governance and storage economics
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Data Engineer / Analytics Engineer / Data Platform Engineer
Benchmark: Modern DE loops: SQL/coding, data modeling, pipelines, distributed systems, reliability, governance and cost 2026 interview refresh: Interview depth now spans both hands-on pipeline work and architecture-level tradeoffs.
Master the interview loop and data-model fundamentals
DE interview process + SQL/coding
Practice: SQL transformations, Python and debugging
Dimensional modeling + lakehouse layers
Practice: grain, keys, facts/dimensions and medallion design
Design reliable batch and streaming pipelines
ETL/ELT + orchestration
Practice: idempotency, schema evolution and recovery
Spark / Kafka distributed processing
Practice: partitions, throughput, lag and backpressure
Defend platform reliability, governance and storage economics
Observability + lineage
Practice: SLAs, quality, incident diagnosis and lineage
Architecture + open-table tradeoffs
Practice: scale, Iceberg, cost/performance and consumers
Interview-Readiness Proof / Practice Loop
Run a SQL/coding mock, then design an end-to-end data platform for batch + streaming. Include contracts, data model, orchestration, quality checks, lineage, recovery, privacy, SLAs and a cost/performance discussion.
Pass Rubric
- SQL/coding fluency
- data-model correctness
- replay/idempotency
- distributed systems reasoning
- SLA/quality evidence
- observability
- governance
- storage tradeoffs
- clear architecture communication