Data QA Manager

Role: Data QA Manager

Location: 100% Remote | quarterly travel to Pittsburgh required

Hours: Comfortable working EST hours

Role Overview

eNGINE is seeking a hands-on Data QA Manager to lead and mature the quality assurance strategy for enterprise data pipelines, platforms, and analytics products.

This is an opportunity to have a significant voice in how a growing Data QA practice is structured, automated, and measured. The ideal candidate combines a strong QA foundation with deep data testing experience and is highly proficient with SQL, Python, PyTest, and Databricks .

The organization is currently heavily manual and is looking for someone who can introduce practical automation, establish repeatable testing standards, and help determine the right tooling and approach for data quality.

You will lead a team of four QA professionals while remaining technically engaged in the work. This role will report to the Director of QA and partner closely with Data Engineering, Data & Analytics, and Data Governance teams.

Responsibilities

• Lead the Data QA function and establish a structured approach to test planning, execution, documentation, and regression coverage.
• Develop and evolve testing strategies for data pipelines, transformation processes, data models, and analytics outputs.
• Use advanced SQL skills to validate tables, transformations, relationships, and downstream data products.
• Build and expand automated data testing capabilities using Python and PyTest , moving the team beyond predominantly manual validation.
• Help evaluate and implement frameworks, tools, and standards for automated data quality testing.
• Establish validation checkpoints throughout the data lifecycle, from ingestion through transformation and final delivery.
• Define practical test coverage around data completeness, accuracy, consistency, uniqueness, and timeliness.
• Validate data within Databricks and other modern data environments.
• Work across transformation and semantic layers to ensure data behaves as expected from source through consumption.
• Establish regression testing for changes to pipelines, source systems, transformations, and data models.
• Partner with Data Engineering to identify opportunities to incorporate testing earlier in the development lifecycle.
• Lead defect identification, prioritization, root-cause discussions, and resolution tracking for data quality issues.
• Establish metrics that demonstrate testing coverage, automation progress, defects, and overall data quality.
• Mentor and develop QA team members while remaining hands-on with complex testing and validation efforts.
• Collaborate with QA leadership and engineering partners to determine the appropriate long-term testing methodology and technology stack.

Required Qualifications

• 7+ years of professional QA/testing experience, with significant experience testing data, data pipelines, or analytics environments.
• Strong background in Data QA or Data Quality ; candidates primarily coming from Data Engineering with limited QA experience will not be a fit.
• Advanced SQL skills and experience validating complex data sets, tables, transformations, and relationships.
• Hands-on Python development experience.
• Hands-on PyTest experience for automated testing.
• Hands-on Databricks experience in a production data environment.
• Experience developing and implementing automated testing for data pipelines or data products.
• Experience with data mapping, data lineage, transformation validation, and downstream data validation.
• Understanding of both transformation and semantic layers within a data architecture.
• Demonstrated ability to establish structured QA practices, including test cases, test plans, regression coverage, and repeatable validation processes.
• Experience leading, mentoring, or managing QA professionals.
• Strong communication skills and the ability to influence how teams approach