Quality Engineer (Data)

Capital Technology Group provides expert consulting services software development, digital transformation, human-centered design, data analytics and visualization, and cybersecurity. Our multidisciplinary teams use agile methodologies to rapidly and incrementally deliver value in close collaboration with our clients. For over a decade, we have been trusted by both federal and commercial clients to solve complex, mission-critical business challenges. The quality of our work has been recognized by our partners and peers through our inclusion in the Digital Services Coalition, a group of forward- thinking firms recognized for excellence in delivering IT services. Client Requirements: applicants MUST BE US Citizens and be able to obtain Public Trust clearance The CTG Experience At Capital Technology Group (CTG), our teams are passionate about modernizing how the federal government delivers software. We partner with federal agencies to build secure, scalable, and mission-driven solutions that make a meaningful impact on millions of people. Recognized by The Washington Post as a Top Workplace in 2025 and 2026. CTG fosters a culture rooted in our core values. Our values guide how we work together and support one another, creating an environment where employees feel trusted, empowered, and encouraged to grow both personally and professionally. About the Role CTG is seeking a Quality Engineer to join a data engineering team, supporting a program that manages financial and regulatory data. This role develops and implements quality assurance strategies, testing methodologies, and automation practices that keep data accurate, complete, consistent, and reliable across modern data pipelines and analytics platforms, combining strong testing and automation skills with data engineering fundamentals and the rigor financial and regulatory data demands. You Will Get To Develop and implement data quality strategies, standards, testing practices, documentation, and maintenance processes for data pipelines and analytical datasets. Design and execute automated and manual tests covering data accuracy, completeness, integrity, uniqueness, schema consistency, business rules, freshness, and statistical validity. Build automated quality checks and integration/end-to-end tests using Python, PySpark/Spark, SQL, and Apache Airflow. Validate data transformations and pipelines across Apache Spark, Python, AWS, Amazon S3, and related AWS data services. Develop reusable testing frameworks and utilities for data pipelines and CI/CD, ensuring code and data are validated before production release. Implement quality validation across raw, cleaned, curated, and analytics-ready data, establishing thresholds, rules, and acceptance criteria. Investigate data anomalies, schema changes, missing data, pipeline failures, and other quality issues; identify root causes and partner with Data Engineers on resolution. Conduct code and product reviews and ensure…