Data Engineering Interviews Are Hard These Days

By /

Listen to this Post

Data engineering interviews have evolved significantly, demanding expertise in SQL, Python/PySpark, system design, pipeline troubleshooting, and cloud architecture. Here’s a breakdown of recent interview expectations and how to tackle them.

Interview Rounds Breakdown

Round 1: SQL Mastery

  • Task: Write a window function to rank orders by customer without ROW_NUMBER.
  • Task: Identify users with consistent purchases over six months.
  • Task: Handle NULLs in joins and aggregations.

You Should Know:

-- Rank orders without ROW_NUMBER 
SELECT customer_id, order_id, 
DENSE_RANK() OVER (PARTITION BY customer_id ORDER BY order_date) AS rank 
FROM orders;

-- Find users with purchases every month 
SELECT user_id 
FROM purchases 
WHERE purchase_date >= DATE_SUB(CURRENT_DATE, INTERVAL 6 MONTH) 
GROUP BY user_id 
HAVING COUNT(DISTINCT DATE_FORMAT(purchase_date, '%Y-%m')) = 6;

-- Handling NULLs in joins 
SELECT a.id, COALESCE(a.value, b.value) AS merged_value 
FROM table_a a 
LEFT JOIN table_b b ON a.id = b.id;

Round 2: Pipeline Design

  • Task: Design a real-time pipeline with exactly-once processing.
  • Task: Hybrid batch-streaming pipeline for clickstream data.
  • Task: Recover a failed transformation pipeline.

You Should Know:

  • Use Kafka with idempotent writes for exactly-once semantics.
  • Spark Structured Streaming + Delta Lake for hybrid pipelines.
  • Implement checkpointing for recovery: 
    spark.readStream 
.format("kafka") 
.option("checkpointLocation", "/path/to/checkpoint") 
.start()

Round 3: Performance & Anomaly Detection

  • Task: Debug a slow Spark job.
  • Task: Detect anomalies in a data stream.
  • Task: Process a 10GB CSV with schema validation.

You Should Know:

Related Posts: