Interviewing guides

Interviewing Data Engineer

Data Engineers play a crucial role in handling and organizing large-scale datasets that drive valuable insights and inform data-driven business decisions.

Key Skills Required for Data Engineers

  • Expertise in database systems and programming languages (e.g., SQL, Python, Java)
  • Experience with big data platforms and tools (e.g., Hadoop, Spark, Kafka)
  • Proficiency in data integration, ETL pipelines, and data warehousing
  • Solid understanding of data modeling and database design
  • Experience with cloud computing platforms (e.g., AWS, GCP or Azure)
  • Problem-solving and analytical skills

Data Engineer Interview Plan and Expectations

Round 1: Resume & Technical Screening (30 minutes)

Objective: Assess the candidate’s background and technical knowledge to determine suitability for the role
  • Languages and tools: SQL, Python/Java, big data technologies (Hadoop, Spark, Kafka), cloud platforms (AWS, GCP, Azure)
Example questions:
  • What big data projects have you worked on, and what was your role in them?
  • Describe your experience with data integration and building ETL pipelines
  • How have you used cloud platforms in your previous work?
Expectations: Clear and concise communication, understanding of technical concepts, relevant experience and background

Round 2: Technical Assessment & Coding Interview (1 hour)

Objective: Evaluate the candidate’s in-depth understanding of data engineering concepts and their ability to code solutions
  • Languages and tools: SQL, Python/Java, data modeling, big data technologies (Hadoop, Spark, Kafka)
Example questions:
  • Write a SQL query to perform a specific analytics task on a large dataset
  • Design an ETL pipeline to process and clean raw data to generate useful insights
  • Code a solution using Python/Java to extract and transform data from multiple sources, and load it into a database
Expectations: Clear understanding of data engineering principles, ability to write efficient queries and pipelines, proficiency in coding with relevant languages

Round 3: System Design & Problem-solving Interview (1 hour)

Objective: Assess the candidate’s ability to design scalable data systems and tackle complex data engineering problems
  • Languages and tools: Data modeling, big data architectures (Hadoop, Spark), cloud platforms (AWS, GCP, Azure), data warehousing, ETL pipelines
Example questions:
  • Design a large-scale data system to process and analyze real-time streaming data
  • How would you optimize a slow-running ETL pipeline to improve its performance?
  • Describe your approach to handling data quality, validation, and monitoring
Expectations: Strong problem-solving skills, ability to design and optimize data systems, extensive knowledge of data engineering concepts and technologies

Important Notes for the Interviewer

  • Ensure to assess the candidate’s ability to communicate complex technical concepts effectively
  • Focus on real-world experiences and their applications of data engineering concepts
  • Consistently address the strengths and weaknesses of different data engineering solutions, considering business requirements and constraints


In conclusion, a successful data engineer candidate will demonstrate a strong grasp of technical concepts, problem-solving skills, and the ability to develop efficient data systems. This interview plan aims to ensure that you thoroughly assess these key competencies throughout the process.

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