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Interviewing ETL Developer in Big Data
An ETL Developer in Big Data is responsible for designing, building, and testing scalable data integration solutions to handle large volumes of structured and unstructured data. They liaise with data engineers, data analysts, and business stakeholders to ensure seamless data flow and optimize data warehouse performance.

Skills Required for ETL Developer in Big Data

  • Experience with ETL tools such as Informatica, Talend, or DataStage
  • Knowledge of big data platforms like Hadoop, Spark, or Flink
  • Proficiency in SQL
  • Scripting languages such as Python or Scala
  • Familiarity with data modeling and data warehousing concepts
  • Effective problem-solving and communication skills

Interview Plan for ETL Developer in Big Data

Round 1: Technical Screening (45 minutes)

Objective: Evaluate the candidate’s ETL and big data knowledge, and programming skills.
  • Questions on ETL concepts and tools
  • Discussion on candidate’s experience with big data platforms and tools
  • Programming questions in SQL or Python/Scala, focused on data manipulation and extraction
Expectations: A clear understanding of ETL processes, hands-on experience with big data platforms, and proficiency in programming

Round 2: In-depth Technical Interview (1 hour)

Objective: Assess the candidate’s ability to design and optimize ETL workflows in a big data environment.
  • Deep dive into candidate’s past ETL projects, including tools, data volumes, and processing times
  • Assess candidate’s understanding of data modeling and data warehousing concepts
  • Technical case study: Design an ETL job for a given big data use case, considering performance, scalability, and data integrity
Expectations: Ability to design complex ETL workflows, knowledge of data warehousing best practices, and ability to address big data challenges

Round 3: Hands-on Coding Test (1.5 hours)

Objective: Examine the candidate’s coding skills in a real-world scenario.
  • Create an ETL script using preferred ETL tool or language, based on a given dataset and requirements
  • Write SQL queries to validate data quality and accuracy
  • Optimize ETL job performance and discuss bottlenecks
Expectations: Well-structured and optimized ETL code, accurate data validation, and effective performance tuning techniques

Important Notes for the Interviewer

  • Evaluate the candidate’s ability to communicate complex technical concepts in a clear and concise manner
  • Assess the candidate’s adaptability to different ETL tools and big data platforms
  • Be aware of recent developments and best practices in the ETL and big data domain


At the end of the interview process, evaluate the candidate’s technical expertise, hands-on experience, and problem-solving abilities. Ensure that the candidate not only understands ETL and big data concepts but also demonstrates the ability to apply these skills effectively in challenging scenarios.
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