Quality Engineer - Data

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 new pipelines and transformations include appropriate unit, integration, and data quality testing.
• Monitor data quality metrics and dashboards and support automated regression testing to protect downstream data consumers.
• Collaborate with Data Engineers, Data Scientists, Analysts, and stakeholders to translate business and regulatory requirements into data quality controls throughout the SDLC.
• Support UAT, release validation, production deployments, and documentation of test strategies, requirements, defects, and validation procedures.

Who You Are

• Passionate about building quality into data and software from the beginning
• A detail-oriented problem solver who enjoys identifying risks and improving processes
• Comfortable collaborating across data engineering, analytics, and stakeholder teams
• Able to balance strategic quality initiatives with hands-on testing responsibilities
• A strong communicator who can clearly document findings and advocate for quality improvements, translating technical data issues for non-technical stakeholders
• Curious about emerging tools, technologies, and testing best practices
• Motivated by mission-driven work and delivering datasets stakeholders can trust

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field  (or equivalent experience)
• 7+ years of professional experience in software quality assurance, testing, or quality engineering roles
• Strong programming experience with Python, and experience developing automated tests and test frameworks (e.g., Pytest or comparable)
• Strong understanding of data quality principles and methodologies, including accuracy, completeness, consistency, uniqueness, validity, and timeliness
• Experience working with relational databases and SQL
• Experience testing data pipelines, ETL/ELT processes, or large-scale data transformations
• Experience with distributed data processing technologies such as Apache Spark/PySpark
• Familiarity with Apache Airflow or another workflow orchestration platform
• Experience working with cloud-based data platforms, preferably AWS
• Experience creating, executing, and maintaining automated and manual test plans and test cases, and identifying, documenting, prioritizing, and tracking software defects
• Experience supporting Agile software development teams
• Strong analytical, communication, and problem-solving skills, including the ability to investigate data discrepancies, identify root causes, and communicate technical data quality issues clearly to both technical and non-technical stakeholders

Nice to Have

• Experience testing financial, securities, regulatory, or other highly governed data
• Experience with AWS services such as Amazon S3, AWS Glue, Amazon Redshift, and/or Amazon Athena
• Experience implementing data quality frameworks or automated data validation platforms
• Experience with Pytest or similar Python testing frameworks
• Experience with dbt and/or automated testing of SQL-based transformations
• Experience with CI/CD pipelines and automated testing within software development workflows
• Experience with data lineage, metadata management, and data observability
• Experience testing data in data lakes, lakehouses, or data warehouses
• Familiarity with schema evolution, schema validation, and detection of schema drift
• Experience designing tests for high-volume datasets and distributed processing environments
• Experience supporting federal government agencies
• Certified Software Tester (CSTE), ISTQB, or similar certification

Salary

We are committed to offering a competitive salary for this position, with an estimated range of $75k–$110k annually. Please note that this range is intended to provide a general idea of what to expect. The final offer may vary based on experience, skills, and other factors.