Description

Own data quality validation, automated testing, and pipeline integrity for analytics and AI outputs.

This role is on-site.

Responsibilities

• Define and maintain automated test suites for data pipelines and model outputs using PyTest and Great Expectations.
• Conduct functional, regression, and integration testing of ETL jobs and data ingestion processes.
• Build automated tools and dashboards for data profiling, drift detection, and quality scorecards.
• Validate data accuracy across Redshift, Postgres, and data marts using SQL and Python.
• Collaborate with data and AI engineers to enforce quality standards and remediate root cause issues.

Required Skills

• 5+ years of experience in QA or Quality Engineering within data-driven environments.
• Proficiency in SQL for validating transformations across Redshift, Postgres, or Snowflake.
• Hands-on experience with test automation frameworks such as PyTest, Great Expectations, or dbt tests.
• Ability to write test cases in Python or other scripting languages.
• Experience with ETL testing, data ingestion pipelines, and data quality validation.
• Familiarity with testing BI dashboards and reports, preferably using Tableau.
• Exposure to CI/CD practices and integrating tests into deployment pipelines.
• Experience using Git for version control and Jira for test tracking.

Preferred Skills

• Experience testing unstructured data pipelines including logs, PDFs, or telemetry.
• Exposure to AWS services including S3, Redshift, and Glue.

Key Skills

Education

Any Gradute