Quality Engineering Lead Data Pipelines and AI Driven Automation

Quality Engineering Lead Data Pipelines and AI Driven Automation

Location : New York City, NY (Hybrid)

Job Summary

• A hands-on QE Lead who blends data quality expertise, Data Pipelines, AI-driven automation, and strong communication skills to build trust in enterprise data and drive continuous quality improvement.
• We are looking for a Quality Engineering Lead with strong hands-on experience in data quality and test automation for data pipelines. This role will lead QE initiatives, leverage AI-driven automation, and work closely with cross-functional stakeholders to ensure high-quality, reliable data products. UI and API testing experience is a plus.

Required Skills & Qualifications

• 10 12 years in Quality Engineering / Test Automation
• Excellent communication skills able to clearly articulate quality status, risks, and recommendations to technical and non-technical stakeholders
• Proficiency in one programming language: JavaScript or Python
• Strong hands-on experience with data pipelines and data quality testing
• Advanced SQL and experience with data warehouses/lakes (Snowflake, Databricks, BigQuery, Redshift)
• Experience using AI/ML concepts or tools in test automation
• Exposure to API automation; UI automation is a plus
• Experience with CI/CD, cloud platforms, orchestration tools (Airflow)and automation frameworks
• Proven stakeholder management and leadership capabilities
  Strong hands-on experience with UI and API automation tools/frameworks (e.g., Cypress, Playwright, Selenium, RestAssured, etc.).
• Expertise in test design methodologies, test data management, and root-cause analysis.
• Proven experience in data testing, including:
• Data normalization techniques
• Data contracts validation
• SQL queries (intermediate or above)

Key Responsibilities

• Lead Quality Engineering strategy and initiatives for data platforms
• Collaborate with Product, Data Engineering, and Business stakeholders
• Hands-on testing and automation of data pipelines (ETL/ELT) must-have
• Implement data quality checks
• Drive AI-enabled test automation (intelligent test generation, anomaly detection, self-healing tests)
• Provide clear communication on quality risks, metrics, and release readiness