Quality Engineering S

Role Name - Quality Engineer

REQUIREMENT_CITY - Philadelphia preferably / Remote

Define, implement, and maintain automated test suites to validate data pipelines, transformations, and analytical outputs.

• Collaborate with data engineers, modelers, and AI engineers to establish and enforce data quality standards and best practices.
• Conduct functional, regression, and integration testing of ETL jobs, data ingestion pipelines, and model outputs.
• Develop test strategies to validate structured (OLTP/OLAP) and unstructured data (logs, PDFs, etc.).
• Build automated tools and dashboards for data profiling, data drift detection, and quality scorecards.
• Identify root causes of data quality issues and coordinate remediation efforts with engineering teams.
• Validate data accuracy across Redshift, Postgres, and data marts using SQL and scripting tools.
• Participate in Agile/Scrum processes including sprint planning, demos, and retrospectives.

5+ years of experience in QA or Quality Engineering roles in data-driven environments.

• MABL, Selenium, PyTest, Great Expectations, dbt tests).
• Proficient in SQL for validating data transformations across platforms like Redshift, Postgres, or Snowflake.
• Familiarity with ETL testing, data ingestion pipelines, and data quality validation techniques.
• Experience testing and validating BI dashboards and reports, preferably using tools like Tableau.
• Exposure to CI/CD practices and integration of tests into deployment pipelines.
• Ability to read and write test cases in Python or other scripting languages.
• Experience testing unstructured data pipelines, including logs, PDF documents, or application telemetry.
• Familiarity with data profiling and anomaly detection tools.
• Exposure to cloud platforms, especially AWS (S3, Redshift, Glue).
• Understanding of analytical data modeling concepts, including Star Schema and OLAP structures.
• Knowledge of testing AI/ML models, particularly for consistency, fairness, or accuracy in predictions.