✅ Strong Answer:
"Built customer 360 pipeline: Kafka → Debezium CDC → S3 raw zone → PySpark silver (cleaning, dedup) → dbt gold (business logic) → Snowflake mart. Airflow DAG orchestrated 50+ tasks. Delta Lake for ACID. Streaming dashboard latency: 6h → 15min. Cost: $120k/mo → $38k/mo (68% savings). 1B events/day processed."
🔥 1️⃣1️⃣ How do you monitor and alert on data pipeline failures?
✅ Answer:
Monitoring stack:
- Data quality: Great Expectations, dbt tests
- Pipeline health: Airflow SLA misses, task failures
- Data freshness: Lag metrics (max(event_time) vs now())
- Volume anomalies: Statistical alerts (±3σ)
Tools: Datadog, PagerDuty, Slack notifications.
Example:
dbt test --store-failures --alert slack.📊 1️⃣2️⃣ What is the medallion architecture? Bronze/Silver/Gold layers
✅ Answer:
Medallion (Databricks): Raw → Clean → Curated.
- Bronze: Raw landing zone (schema-on-read).
- Silver: Cleaned, deduplicated, enriched.
- Gold: Business-ready marts (aggregations, joins).
Example:
bronze_events → silver_events (dedup) → gold_customer_daily (business KPIs).🧠 1️⃣3️⃣ Compare ACID transactions across different data systems
✅ Answer:
- Traditional RDBMS: Full ACID.
- Data Lakes: None (eventual consistency).
- Delta Lake/Iceberg: ACID via transaction log.
- Snowflake: Time Travel ACID (query past states).
- Kafka: Exactly-once with idempotent producers.
Choose based on consistency vs scale needs.
📈 1️⃣4️⃣ How do you optimize Spark jobs for cost and performance?
✅ Answer:
Cost: Auto-scaling clusters, spot instances, partition pruning.
Performance:
- Cache/persist intermediate results
- Broadcast small tables for JOINs
- Predicate pushdown (filter before join)
- Adaptive query execution (AQE)
- Z-order clustering
Monitor: Spark UI, Ganglia, query profiles.
📊 1️⃣5️⃣ What tools and tech stack do you use daily?
✅ Answer:
- Orchestration: Airflow, Prefect, Dagster
- Processing: PySpark, dbt, DuckDB
- Storage: S3, Snowflake, Delta Lake, PostgreSQL
- Streaming: Kafka, Flink, Kinesis
- Cloud: AWS/GCP/Azure (EMR, Databricks, VertexAI)
- Monitoring: Datadog, Grafana, Great Expectations
💼 1️⃣6️⃣ Describe a challenging data engineering problem you solved
✅ Answer:
"Production pipeline failed silently dropping 30% events due to Kafka consumer lag (7-day backlog). Root cause: Spark Structured Streaming micro-batch outpacing consumer group.
Fix: Dynamic partitioning by watermark, exactly-once semantics, consumer group rebalancing. Added dead letter queue, lag monitoring alerts.
Result: 99.99% delivery guarantee, processing resumed in 4 hours vs 7 days. Implemented chaos testing for future resilience."
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