Data QA : Raleigh, NC (Onsite)

Job Title: Data QA

Location: Raleigh, NC

Key Responsibilities

• Design & execute test plans for ETL/ELT workflows, data feeds, and batch extracts, ensuring comprehensive data validation
• Write and maintain manual and automated tests for data accuracy, completeness, and consistency against mapping documents and specification rules
• Develop SQL-based validation logic—supporting complex queries, joins, aggregates, and stored procedures—to verify data correctness
• Collaborate with data engineering, BI, architecture teams to identify and resolve data quality issues across data pipelines
• Develop or support CI/CD pipelines for automated data quality checks and data-flow validations
• Analyze test results, document defects, track resolution, and report status to stakeholders; act as advocate for quality in an Agile delivery process 

Required Skills & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 5–6+ years of experience in QA, SDET, or data quality roles—preferably in data engineering/test test automation in the financial services industry
• Strong programming and automation experience in Python or Java, with focus on testing frameworks and automation tools (e.g. Selenium, TestNG, Appium)
• Advanced SQL skills, capable of building complex queries to validate ETL, data loads, and data transformations
• Handson with ETL/Database testing—manual & automated—covering batch pipelines, API integrations, and data feeds
• Experience with AWS-based data environments (e.g. S3, RDS, DynamoDB, Lambda, Snowflake, Redshift) and familiar in working with data lakes
• Knowledge of CI/CD tools (Jenkins, TeamCity, GitHub Actions, Octopus), test automation in pipelines, and Agile methodologies including Scrum ceremonies

Preferred Qualifications

• Familiarity with data quality tools, metrics frameworks, rule validation engines, or profiling platforms
• Exposure to big data technologies—such as Hadoop, Snowflake, DBT, Kafka, DBT, Airflow, FiveTran, PySpark—for data testing contexts
• Understanding of dimension modeling (star schema, SCD), data governance standards, metadata management, and change data capture patterns
• Financial services domain experience and familiarity with regulatory/operational compliance testing contexts