Describe a data-heavy project

How to structure your answer (2–3 minutes) - One‑liner: Project name and what it enables for the business. - Objective: Why it mattered and how success was measured. - Your role: Ownership scope and who you partnered with. - Architecture & stack: Ingestion → processing (batch/stream) → storage → consumption; highlight 2–3 design choices. - Dataset: Volume (rows/day, TB/day), velocity (p95 latency), variety (schemas/sources), constraints (PII, GDPR). - Challenges → Decisions: Top 3 issues, trade‑offs, and rationale. - Results: Quantified improvements (latency, cost, reliability, accuracy, adoption). Sample answer (Data Engineer, consumer‑scale product) 1) Summary - I led the design and rollout of a near‑real‑time engagement analytics pipeline that powers dashboards, A/B testing, and model features for a short‑form video feed. 2) Objectives and success criteria - Reduce end‑to‑end event latency from ~25 minutes to under 5 minutes p95 to unblock near‑real‑time monitoring and fresher model features. - Improve data quality (duplicates, late/out‑of‑order events, schema drift) with <0.1% duplication and >99.9% completeness. - Enable compliant, row‑level user deletion (GDPR/CCPA) without full table rewrites. 3) My responsibilities - Tech lead for ingestion and streaming layers: architecture, implementation, on‑call SLOs, and incident response. - Drove data contracts with client/SDK teams; partnered with data science and ML to define feature needs and SLOs. - Implemented quality/monitoring, backfills, and cost controls; mentored two engineers. 4) Architecture and stack - Ingestion: Kafka (topics per event family, 96–192 partitions), Protobuf schemas in Schema Registry. - Stream processing: Apache Flink (stateful, event‑time), RocksDB state backend, exactly‑once checkpoints; watermark = 10 minutes; dedup + sessionization + feature aggregation. - Storage: S3 + Apache Iceberg tables (Parquet, ZSTD), partitioned by date_hour and bucket(user_id, 32); Iceberg row‑level equality deletes for compliance. - Batch/backfill: Spark 3 + Airflow for daily compaction, CDC replays, and historical rebuilds. - Serving/Query: Trino/Presto for analysts and experiment platform. - Quality & Monitoring: Great Expectations + custom invariants; Prometheus/Grafana + alerting on volume, latency, staleness, null‑rate, schema changes. 5) Dataset characteristics - Volume: ~5–7B events/day (~4–6 TB/day compressed Parquet); peak throughput 250–400K events/sec. - Variety: 60+ event types (view, like, share, comment, watch_time), evolving schemas. - Velocity: Required p95 < 5 minutes pipeline latency; observed late arrivals up to 60 minutes; client clock skew common. - Constraints: PII minimization, GDPR/CCPA deletions, regional data residency, cost budget. 6) Key challenges and decisions - Data quality: Duplicates from client retries and network timeouts; late/out‑of‑order events. - Decision: Idempotency via event_id; Flink keyed dedup with state TTL and watermark = 10 minutes. - Optimization: Bloom filter per shard to bound memory for short dedup windows. Quick calc: for 60M events in 10 minutes at peak and 1% false‑positive rate, bits/item ≈ 9.586; memory ≈ 60M × 9.586 bits ≈ 575M bits ≈ 72 MB; ~7 hash functions. - Guardrail: If event_time > processing_time + 2h, route to quarantine for review. - Scale & small files: Many small Parquet files spiked query cost and metastores. - Decision: Iceberg partitioning (date_hour + hash bucket), writer targets of 256 MB, and scheduled compaction; vectorized reads + ZSTD level 3. - Latency vs reliability: Exactly‑once sinks increased checkpoint overhead. - Decision: 2‑phase commit to Iceberg with 60s checkpoints; tuned async I/O and backpressure; accepted p99 ~ 6–7 minutes to keep p95 < 5 minutes. - Schema evolution & contracts: Uncoordinated SDK updates broke downstream jobs. - Decision: Backward‑compatible Protobuf; required data contracts (field ownership, defaults, nullability); CI schema checks block deployments. - Privacy/Compliance: Efficient user deletion without nightly rewrites. - Decision: Iceberg equality deletes with periodic compaction; PII tokenization at ingestion; data retention policies by region. 7) Results (measurable) - Latency: Reduced p95 from ~25 minutes to 2.8 minutes; p99 ≈ 6.1 minutes. - Quality: Duplicate rate down from ~1.3% to 0.06%; >99.95% row availability by T+5 minutes; late‑event handling improved feature accuracy by ~0.7 pp. - Cost & reliability: Storage cost −28% via ZSTD + file sizing; query cost −22% via partitioning and compaction; 99.95% pipeline availability with automated retries and checkpoint tuning. - Adoption & impact: 12+ downstream teams migrated; fresher features lifted online CTR by ~1.2% in A/B tests; incident volume down ~40% quarter‑over‑quarter. 8) Lessons learned - Data contracts and schema governance prevent most breakages; invest early. - Exactly‑once semantics are expensive; target them where replays are costly and use idempotent sinks elsewhere. - Design for compaction from day one to avoid small‑file debt. Guardrails/validation to mention if probed - Canary topics and shadow pipelines during migrations; automated drift detection on cardinality and null distributions. - SLOs: p95 latency, data completeness, and freshness; error budgets tied to on‑call. - Backfills are idempotent: write to staging tables, validate with row‑count and checksum parity, then swap. Brief template you can adapt - Objective: Build X to achieve Y (metric/SLO). - Role: I owned A, partnered with B. - Stack: Ingestion → Processing → Storage → Orchestration → Monitoring. - Data: N events/day, V TB/day, p95 latency SLO, late events %, PII constraints. - Challenges → Decisions: 1) quality, 2) scale/latency, 3) cost/privacy; why these choices. - Results: Latency, quality, cost, reliability, adoption—each with numbers. This structure shows you can translate business goals into a scalable, reliable data system, make explicit trade‑offs, and quantify impact.