Capitaltg Quality Engineer (Data) at Capital Technology Group supporting financial and regulatory data programs. Develop and implement quality assurance strategies and automation for data pipelines and analytics platforms.
Responsibilities
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.
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.
Qualification
Curious about emerging toolsBachelor's degree in Computer ScienceExperience testing financialExperience with data lineageExperience testing data in data lakes
Required
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
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
Preferred
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
