Quality Engineer (Data)

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.

Responsibilities

• 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.

Requirements

• 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