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Built by a hiring manager who's conducted 1,000+ interviews at Google, Amazon, Nvidia, and Adobe.
Last updated: December 9, 2025
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Data engineering interviews assess your ability to design, build, and maintain scalable data pipelines and infrastructure that enable analytics and machine learning. Expect questions covering ETL/ELT processes, data warehousing, big data technologies, data modeling, and data quality. Success requires demonstrating both technical proficiency with data tools and frameworks alongside understanding of distributed systems, performance optimization, and business requirements.
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ETL extracts data, transforms it outside target system, then loads. ELT loads raw data first, then transforms within target. Use ETL when transformations complex/resource-intensive, target system has limited compute, or need data cleansing before loading. Use ELT when target system powerful (modern data warehouses like Snowflake, BigQuery), want faster initial load, leverage warehouse optimization, or need flexibility in transformations. ELT increasingly common with cloud warehouses. Discuss modern data stacks favoring ELT with tools like dbt.
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Design with stages - ingestion (batch or streaming based on latency requirements), storage in data lake (S3, ADLS), processing with Spark or similar for transformations, load to warehouse (partitioned tables), orchestration with Airflow. Consider idempotency for retry safety, incremental processing vs full refresh, data validation and quality checks, schema evolution handling, monitoring and alerting, backfill strategy. Partition data by date for performance. Use columnar formats (Parquet, ORC). Implement data lineage tracking. Discuss scaling horizontally and cost optimization.
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SCD handles changes to dimension attributes over time. Types - SCD Type 1 (overwrite, no history), Type 2 (add new row with effective dates, maintains history, most common), Type 3 (add columns for previous values, limited history). For Type 2, include surrogate key, effective start/end dates, current flag. Use case determines type - customer address might be Type 1, pricing history Type 2. Implementation - compare source with target, insert new rows for changes, update end dates. Discuss impact on storage and query complexity.
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Systematic approach - analyze execution plan (EXPLAIN/EXPLAIN ANALYZE), identify bottlenecks (table scans, sorts, joins), check statistics are current, add appropriate indexes (covering indexes for frequently queried columns), rewrite query (eliminate subqueries, use CTEs, avoid SELECT *), partition large tables, denormalize if appropriate, use materialized views for complex aggregations, ensure join columns indexed and same type, limit result set early in query. Profile query with actual data volumes. Discuss trade-offs between read and write performance.
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Batch processes large volumes of data at scheduled intervals (hourly, daily), higher latency but higher throughput, good for historical analysis, simpler to implement and debug. Stream processes data in real-time as it arrives, low latency, handles continuous data, more complex. Use batch for: reporting, data warehousing, ETL jobs, ML training. Use stream for: fraud detection, real-time dashboards, event-driven applications, time-sensitive analytics. Often use both: streaming for real-time, batch for corrections and historical backfill. Discuss Lambda and Kappa architectures.
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Partitioning divides large tables into smaller, manageable pieces based on column values (typically date/time), improving query performance and management. Benefits include partition pruning (scanning only relevant partitions), parallel processing, easier maintenance (archive old partitions), faster deletes. Strategies - range (dates), hash (distribute evenly), list (specific values). Choose partition key based on query patterns. Over-partitioning creates overhead. Discuss partition size (aim for 100MB-1GB), compaction strategies, and metastore performance.
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Implement validation at multiple stages: schema validation (data types, required fields), completeness checks (null values, missing records), accuracy checks (range validation, referential integrity), uniqueness constraints, timeliness monitoring, consistency across sources. Use data quality framework (Great Expectations, deequ), define SLAs for data freshness, implement automated alerts on quality violations, create data quality dashboards, track metrics over time. Reconcile record counts between stages. Quarantine bad data for investigation. Discuss data quality as ongoing process requiring monitoring and iteration.
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CAP theorem states distributed systems can provide only two of - Consistency (all nodes see same data), Availability (system remains operational), Partition tolerance (works despite network failures). Since partitions inevitable in distributed systems, choose CP (consistent but may be unavailable during partition) vs AP (available but may be inconsistent). Examples - traditional RDBMS prioritize consistency, Cassandra/DynamoDB favor availability. Choose based on application needs - financial transactions need consistency, social media can tolerate eventual consistency. Discuss eventual consistency and conflict resolution strategies.
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Spark is distributed computing framework for big data processing. Unlike MapReduce which writes intermediates to disk, Spark uses in-memory computation with RDDs/DataFrames for iterative algorithms. Benefits: 10-100x faster for iterative workloads, unified API for batch/streaming/ML, richer transformations. Spark suited for: iterative ML algorithms, interactive analytics, complex DAGs. MapReduce better for: one-pass processing, when memory limited. Spark architecture includes driver, executors, cluster manager. Discuss lazy evaluation, transformations vs actions, and optimization with Catalyst and Tungsten.
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Use STAR method describing specific failure (data loss, pipeline stall, data quality issue, performance degradation). Explain debugging approach: reviewing logs, checking monitoring dashboards, analyzing data samples, verifying dependencies, testing components in isolation. Describe root cause (schema change, resource exhaustion, incorrect transformation logic), solution implemented, validation approach, and preventive measures (better testing, monitoring alerts, documentation). Quantify impact and recovery time. Emphasize systematic troubleshooting, communication with stakeholders, and learning from incident.
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Strategies include: schema registry (Confluent Schema Registry) for centralized management, versioning schemas, backward/forward compatibility rules, using formats supporting schema evolution (Avro, Parquet), implementing schema validation in pipeline, graceful degradation for unknown fields. Approaches: adding optional fields (backward compatible), evolving types carefully (e.g., int to long okay, long to int breaks), removing fields (forward compatible). Test schema changes in non-prod first. Use tools like dbt for migration management. Discuss communication with data producers and consumers about changes.
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Data lake stores raw data in native format (structured, semi-structured, unstructured), schema-on-read, flexible but requires more processing. Data warehouse stores structured data with schema-on-write, optimized for analytics, higher performance but less flexible. Use lake for: raw data storage, diverse data types, exploratory analysis, ML training data, cost-effective storage. Use warehouse for: BI and reporting, well-defined schemas, production analytics, guaranteed performance. Modern approach: lakehouse combining benefits using Delta Lake, Iceberg, or Hudi. Medallion architecture uses both: lake for bronze/silver, warehouse for gold.
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Architecture includes event streaming (Kafka), stream processing (Flink, Spark Streaming, or Kafka Streams), real-time storage (Redis, Druid), analytics layer (materialized views, aggregations), API layer for queries. Key considerations - exactly-once semantics, state management and checkpointing, late data handling (watermarks), windowing strategies (tumbling, sliding, session), scalability and backpressure handling, monitoring lag and throughput. Trade-offs between latency, throughput, and accuracy. Discuss complementing with batch processing for corrections and Lambda/Kappa architecture choices.
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Consider query patterns (columnar formats like Parquet, ORC for analytics with selective column reads; row-based like Avro for write-heavy or full-row access), compression efficiency (columnar compresses better), schema evolution support (Avro strong), ecosystem compatibility (Parquet widely supported in Spark, Athena; ORC optimized for Hive), read vs write performance trade-offs, and splittability for parallel processing. Parquet generally good default for analytics. Avro for streaming. ORC if heavily in Hive ecosystem. Discuss encoding techniques (dictionary, run-length) and file size considerations for cloud storage costs.
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Implement using tools like Apache Atlas, Amundsen, or DataHub. Capture metadata at pipeline execution time including source tables, transformations applied, output tables, timestamps, and quality checks. Track column-level lineage for regulatory compliance. Store in centralized metadata repository. Create data catalog for discovery with business descriptions, owners, and SLAs. Implement impact analysis to understand downstream effects of changes. Use tags for PII classification. Expose via UI for data discovery and trust. Discuss importance for troubleshooting, compliance, and collaboration between teams.
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