Can you describe your data engineering experience and tell me about a project you worked on?

S – In my previous role at a mid‑size e‑commerce company, we had a growing data lake that was fragmented across multiple sources (clickstream logs, transactional data, and third‑party marketing data). The data was not easily consumable by analysts, leading to slow reporting.

T – I was tasked with designing a unified, scalable data pipeline that would ingest, transform, and expose the data to downstream analytics and ML teams.

A – I chose an Airflow‑based orchestration layer to schedule daily ETL jobs. For ingestion, I used Kafka to stream real‑time clickstream events and Spark Structured Streaming to process them. For batch data, I leveraged Spark jobs that read from S3, performed schema evolution, and wrote Parquet files partitioned by date and region. I implemented data quality checks using Great Expectations and stored metadata in a Hive metastore. Finally, I exposed the cleaned data via a Snowflake data warehouse, creating materialized views for common reporting slices.

R – The new pipeline reduced data latency from 48 hours to 2 hours, increased data quality scores by 30%, and cut storage costs by 15% through efficient partitioning and compression. Analysts could now generate dashboards in minutes, and the ML team could train models on up‑to‑date data, improving recommendation accuracy by 12%.

This experience taught me the importance of end‑to‑end ownership, from ingestion to consumption, and reinforced best practices in data quality, scalability, and automation.