Top 20 Interview Questions & Answers for Data Engineer (2025)

A Data Engineer plays a critical role in designing, building, and managing scalable data pipelines and infrastructure for analytics and machine learning applications. The following list contains a blend of behavioral, situational, technical, and cultural fit questions tailored to evaluate your readiness for a Data Engineering position in 2025.

Behavioral Questions

• Tell us about a data pipeline you built from scratch.
  I designed and deployed a scalable ETL pipeline using Apache Airflow and Spark that processed 50M+ records daily, improving reporting efficiency by 40%.
• Describe a time you resolved a critical production issue.
  During a major outage, I traced a bottleneck in a Kafka consumer group and reconfigured the partition handling, restoring flow within 30 minutes.
• How have you collaborated with data scientists or analysts?
  I partnered with analysts to re-structure data marts in Redshift, enabling self-service BI and reducing dependency on engineering for queries.
• What project are you most proud of?
  I built a data lake architecture on AWS S3 with Glue and Athena that cut down storage costs and improved querying performance.
• Have you ever had to explain a technical decision to a non-technical stakeholder?
  Yes, I explained the shift from batch to streaming ETL by comparing it to just-in-time data delivery, gaining executive buy-in for the transition.

Situational Questions

• What would you do if a critical ETL job failed during peak hours?
  I’d immediately check logs and alerts, rerun manually if needed, and communicate the incident with impact estimates to the stakeholders.
• How would you handle a request for real-time data when your system is batch-based?
  I’d evaluate whether change data capture (CDC) and streaming tools like Kafka or Kinesis can integrate into the current architecture incrementally.
• If storage costs rise rapidly, how would you respond?
  I’d identify cold and redundant data using lifecycle policies and leverage compression formats like Parquet or ORC for efficient storage.
• How would you deal with conflicting data from two sources?
  I'd investigate data lineage, validate source integrity, and work with domain experts to define a rule-based reconciliation strategy.
• You are asked to onboard a new data source quickly. What’s your approach?
  I’d use schema inference tools or data profiling to understand the source, automate ingestion via Airflow, and validate sample data post-load.

Technical Questions

• What’s your experience with data warehouses?
  I've worked extensively with Redshift, BigQuery, and Snowflake to design star schemas and optimize complex analytical queries.
• Explain the difference between OLTP and OLAP systems.
  OLTP handles transactional, real-time data updates; OLAP is optimized for analytical querying and reporting across large historical datasets.
• How do you ensure data quality?
  I implement validation rules, schema checks, null audits, and unit tests in ETL pipelines using tools like Great Expectations.
• Describe a well-optimized data model.
  A normalized model for ingestion and a denormalized or star schema for analytics to balance performance and flexibility.
• What is your experience with cloud platforms?
  I’ve built data solutions using AWS (S3, Glue, Lambda), GCP (BigQuery, Dataflow), and Azure (Data Factory, Synapse).

Cultural Fit Questions

• How do you stay updated in the fast-changing data engineering field?
  I follow blogs, take courses on new tech like dbt and Delta Lake, and contribute to open-source data tools.
• Describe your ideal work culture.
  A culture that values transparency, continuous learning, and cross-functional collaboration aligns best with how I work.
• How do you prioritize work under tight deadlines?
  I use impact-effort matrices and communicate trade-offs clearly with stakeholders to manage delivery expectations.
• What does ownership mean to you in a data team?
  It means being responsible for data integrity end-to-end—from ingestion to delivery and communicating when issues arise.
• How do you handle disagreements in a team setting?
  I promote evidence-backed discussions, stay open to alternative views, and focus on collective success over being right.