Role: QA Manager – Data Warehouse Testing
Location: 100% Remote (East Coast Working Hours)
Position Type: Multi-Year Contract
Leadership/Technical Split: 60% Management / 40% Hands-on
Notes:
They do not need to have financial services experience for this job, but would be a big plus.
Please focus on the Datawarehouse/ETL testing – building the strategy around it, while still being hands on.
Position Overview
Our Client is seeking an experienced, hands-on QA Manager to lead quality assurance initiatives within its Data Warehouse team.
This individual will be responsible for establishing QA strategy, implementing testing processes and best practices, and managing a small team of nearshore QA resources.
The ideal candidate brings a strong background in data warehouse testing, ETL/ELT validation, data quality, and test automation, combined with proven experience building and leading QA functions.
This is a leadership position that requires both strategic ownership and technical execution.
Approximately 60% of the role will focus on team management, QA strategy, and process improvement, while 40% will involve hands-on testing, automation development, and technical oversight.
We are looking for someone who approaches quality assurance with a data-first mindset, understands how enterprise data platforms operate, and can establish scalable testing practices across a modern Azure and Databricks environment.
Key Responsibilities
Data Warehouse Testing & Quality Assurance
- Lead QA efforts across the Data Warehouse team, ensuring the accuracy, integrity, reliability, and consistency of enterprise data.
- 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 reconciliation of Power BI reports against underlying data sources.
QA Leadership & Strategy
- 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 overall 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.
Hands-on Testing & Automation
- Remain actively involved in technical QA activities, including writing, reviewing, and maintaining automated tests.
- 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.
Required Qualifications
- 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, ideally including distributed, offshore, or nearshore resources.
- 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.
Preferred Qualifications
- 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.
Technology Environment
Data & Analytics: Databricks, SQL, Spark SQL, Unity Catalog, Power BI
Test Automation: Python, PyTest, Playwright, Requests, Swagger/OpenAPI
Cloud & DevOps: Microsoft Azure, Azure DevOps, GitHub, CI/CD Pipelines
QA Management & AI Tools: Qase, Claude Code
Ideal Candidate Profile
The ideal candidate is an experienced QA Manager or QA Automation Lead with a strong foundation in data warehouse and data pipeline testing.
They have successfully led QA teams, established testing processes, and developed automated data validation solutions.
They are comfortable working directly with data engineers and business stakeholders, can troubleshoot complex data quality issues, and remain technically hands-on with Python and SQL.
Above all, this individual should be capable of owning the QA function from both a strategic and technical perspective, building scalable processes, developing their team, and driving continuous improvement within a modern enterprise data environment.
