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Capital Technology Group

Quality Engineer (Data)

Remote (US) (Remote)$75k–$110kmidAdded today

About this role

Capital Technology Group seeks a Quality Engineer to join a data engineering team supporting financial and regulatory data systems. You'll develop and implement comprehensive QA strategies, automated testing frameworks, and validation processes across modern data pipelines and analytics platforms using Python, Spark, SQL, and AWS.

What you'll do

  • Design and execute automated and manual tests for data accuracy, completeness, integrity, consistency, and business rule validation
  • Build automated quality checks and integration tests using Python, PySpark, SQL, and Apache Airflow
  • Develop reusable testing frameworks and utilities for data pipelines and CI/CD workflows
  • Validate data transformations across Spark, Python, AWS, and S3 environments
  • Establish data quality standards, documentation, and maintenance processes for production pipelines
  • Collaborate with data engineering, analytics, and stakeholder teams to identify and communicate quality issues

What they're looking for

  • Python programming and test automation (Pytest or similar)
  • Apache Spark / PySpark
  • SQL and relational databases
  • Apache Airflow or workflow orchestration
  • AWS data services and S3
  • Data quality principles and methodologies
  • ETL/ELT pipeline testing
  • CI/CD practices and automation
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Capital Technology Group

Capital Technology Group builds secure, scalable technology solutions for federal government modernization initiatives, with particular expertise in Identity & Access Management systems. The company is hiring quality assurance and solutions architecture roles to lead technical delivery on mission-critical government programs.

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Likely interview questions

  • Walk us through your experience testing ETL or data pipeline processes—what challenges did you encounter and how did you address them?
  • Describe a time you developed an automated testing framework for data transformations. What tools did you use and what metrics did you track?
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