Data pipeline broken in production.Fixed this session.

PM assigned in 10 minutes, data engineer begins diagnosis within the hour. Airflow DAG failures typically stem from: a task timeout where the execution_timeout is set shorter than the actual runtime, an upstream task failure propagating through depends_on_past=True blocking all downstream runs, a Python import error in the DAG file itself preventing the scheduler from parsing it (visible in the Broken DAGs section of the Airflow UI), or a database connection failure because the Airflow metadata DB ran out of connections. The engineer inspects the task instance logs, checks scheduler logs for parse errors, and traces the failure chain. Most DAG failures are resolved in a single 4-hour session.

Yes. Spark executor OOM is caused by data skew pushing too much data to one partition, insufficient executor memory allocation for the data volume, or a collect() or toPandas() call pulling a large dataset onto the driver. The engineer inspects the Spark UI to identify the stage with the largest task size, checks partition size distribution for skew, and applies the right fix: salting the join key for skew, increasing spark.executor.memory and spark.executor.memoryFraction, replacing collect() with a distributed write, or switching to broadcast join for small-dimension tables. A repartition or coalesce strategy is also applied where the output partition count is unbalanced.

Yes. Unexpected BigQuery cost spikes are caused by: a query with a SELECT * on a wide table that scans terabytes instead of using column pruning, a missing partition filter causing a full-table scan on a partitioned table, a scheduled query that was modified to run hourly instead of daily, or a new pipeline writing to an unpartitioned table and scanning it without a filter. The engineer uses INFORMATION_SCHEMA.JOBS_BY_PROJECT to identify the top cost-generating queries, INFORMATION_SCHEMA.TABLE_STORAGE_BY_PROJECT to find unexpectedly large tables, and recommends: adding partition filters, replacing SELECT * with explicit columns, enabling require_partition_filter on large tables, and setting byte billing limits per query.

Kafka consumer lag growth means consumers cannot keep up with the producer throughput. Root causes include: the consumer group has too few partitions to parallelize across (consumer count cannot exceed partition count), the consumer is doing expensive synchronous processing per message instead of batch processing, a downstream database write is slow and blocking the poll loop, or the consumer crashed and the group is in a rebalance storm because session.timeout.ms is too low. The engineer uses kafka-consumer-groups.sh --describe to see per-partition lag, checks consumer application metrics for processing time per message, and applies the right fix: increasing partition count, enabling batch commit, tuning max.poll.records, or fixing the slow downstream call.

Every QuickHire data engineer passes: a live debugging exercise on a broken Airflow pipeline with a hidden DAG parse error and a Spark data skew problem, a written assessment covering SQL window functions and query optimization, Spark internals (shuffle, partitioning, catalyst optimizer), Kafka consumer group semantics (offsets, rebalance, at-least-once vs exactly-once), and dbt model materialization strategies (incremental vs table vs view), plus a portfolio review of production data pipelines. Only the top 3% of applicants pass all stages. PM matches engineers to your exact stack - Airflow version and operator library, Spark on EMR vs Dataproc vs Databricks, BigQuery vs Snowflake vs Redshift, and dbt Cloud vs dbt Core.

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