QA Manager – Data Warehouse Testing

3Core Systems , Inc

3d•Remote

About The Position

The client is seeking an experienced, hands-on QA Manager to lead quality assurance initiatives within its Data Warehouse team. The role establishes QA strategy, implements testing processes and best practices, and manages a small team of nearshore QA resources. It combines strategic ownership and technical execution, with approximately 60% focused on team management, QA strategy, and process improvement and 40% on hands-on testing, automation development, and technical oversight. The position emphasizes a data-first approach and scalable testing practices across an Azure and Databricks environment. The QA Manager leads data warehouse quality efforts, develops and executes testing strategies for data warehouses and ETL/ELT workflows, and builds automated data validation solutions. The position also oversees QA processes and metrics, mentors QA engineers, collaborates with engineering and business stakeholders, and remains hands-on with SQL, Python, Pytest, and other testing tools. The position is 100% remote, with East Coast working hours, and is a contract role. Financial services experience is not required but is a plus.

Requirements

At least 8 years of experience in Quality Assurance, including at least 3 years in a hands-on QA Lead or QA Manager position.
Strong experience with data warehouse testing, ETL/ELT testing, data validation, and data quality assurance.
Proven ability to establish QA strategies, implement processes, and improve or build QA functions.
Experience managing and mentoring QA engineers; distributed, offshore, or nearshore team experience is advantageous.
Strong hands-on SQL experience, including data reconciliation, transformation validation, and data quality testing.
Proficiency in Python and Pytest for test automation.
Experience developing and maintaining automated testing frameworks.
Experience with API, database, and web application testing.
Experience with Playwright for UI automation.
Experience testing data pipelines and applications in cloud environments.
Strong understanding of Agile development methodologies and QA best practices.
Ability to balance strategic leadership responsibilities with hands-on technical contributions.
Excellent communication, stakeholder management, and organizational skills.
Ability to identify resource needs, communicate technical challenges, and recommend improvements to leadership.
Willingness to learn and adopt new technologies and AI-assisted development tools.

Nice To Haves

Hands-on experience testing Databricks environments, including notebooks, jobs, pipelines, and Unity Catalog.
Experience with Azure cloud services and modern cloud-based data platforms.
Familiarity with Spark SQL and Databricks data quality and automated testing frameworks.
Experience validating Power BI reports and analytics data.
Experience with CI/CD tools such as GitHub Actions or Azure DevOps.
Familiarity with test management tools such as Qase.
Experience with performance testing and troubleshooting.
Experience building QA teams or testing functions from the ground up.
Experience with AI-assisted development tools such as Claude Code.
Financial services experience.

Responsibilities

Lead QA efforts across the Data Warehouse team to ensure enterprise data accuracy, integrity, reliability, and consistency.
Develop and execute testing strategies for data warehouses, ETL/ELT processes, Databricks notebooks, data pipelines, and transformations.
Design and automate data validation frameworks, including source-to-target reconciliation, data quality checks, and transformation testing.
Validate Databricks jobs, pipelines, Unity Catalog components, and associated data workflows.
Use SQL and Python to develop automated testing solutions, identify data discrepancies, and troubleshoot data quality issues.
Validate downstream reporting and analytics, including reconciling Power BI reports against underlying data sources.
Establish and continuously improve QA processes, testing standards, automation frameworks, and best practices.
Lead, mentor, and manage a small team of nearshore QA engineers, overseeing work allocation, priorities, deliverables, and performance.
Evaluate the current QA landscape, identify testing gaps, and implement improvements to strengthen quality and efficiency.
Assess team capacity and resource requirements, including developing business cases for additional headcount when necessary.
Define and monitor QA metrics, test coverage, defect trends, and release readiness.
Partner with data engineers, developers, and business stakeholders to align QA practices with business requirements.
Write, review, and maintain automated tests and remain actively involved in technical QA activities.
Develop automation using Python, Pytest, and SQL.
Perform API, database, and web application testing as needed.
Support UI automation using Playwright for internal applications and reporting interfaces.
Review automation code, establish coding standards, and optimize existing test suites.
Integrate testing into development and deployment workflows, including CI/CD pipelines.
Evaluate and adopt emerging QA technologies and AI-assisted development tools, including Claude Code, to improve testing efficiency and coverage.