Senior Data QA Engineer

We're looking for a US based Senior Data QA Engineer to join our high-performing, fully remote engineering team. This is a senior-level role for someone who thrives in fast-paced environments, excels at solving complex problems, and is comfortable leading technical initiatives across product, data, and analytics domains through both technical expertise and an inquisitive, curious mindset. In this role, you'll be expected to take ownership of QA processes for mission-critical projects-ranging from customer-facing applications to backend systems and large-scale data workflows. You'll collaborate closely with cross-functional teams to design scalable QA strategies, guide their execution, and ensure best practices are applied across the stack and teams. You should bring a strong background in quality assurance with hands-on experience in data validation, test automation, and modern QA practices across APIs, data pipelines, and web applications. The ideal candidate is a confident QA leader-comfortable working in complex systems, resolving ambiguity, and driving clarity through structured test strategies. You'll not only define and execute comprehensive test plans, but also help shape how we ensure data and system quality at scale. You'll be responsible for:

• Designing and owning data QA processes, including test documentation, incident tracking, and quality metrics and dashboards.
• Design and implement end-to-end test strategies for data pipelines, ETL/ELT processes, data warehouses, and reporting systems.
• Building automated data validation frameworks to ensure accuracy, consistency, and reliability of large-scale datasets
• Creating and maintaining data quality checks, test plans, and monitoring solutions across structured and semi-structured data sources
• Leading QA initiatives across data-focused projects, collaborating closely with Data Engineering, Engineering, Analytics, and Project management teams
• Developing SQL- and script-based test suites to validate data transformations, business logic, and aggregation rules.
• Supporting analytics, BI, and reporting platforms by validating data integrity, lineage, and metric correctness
• Proactively identifying data quality issues and driving resolution through detailed root cause analysis and stakeholder coordination
• Integrating automated data tests into CI/CD workflows to enable continuous data validation and faster releases
• QA best practices in the data domainMentoring other QA team members on data validation techniques, automation strategies, and QA best practices in the data domain

Ideal candidate requirements: Technical Skills

• 7+ years of experience in QA area
• 4+ years of experience as a Data QA Engineer/Data QA Analyst
• Deep understanding of general QA process, test design techniques and industry standards.
• Strong expertise in scripting programming languages (JavaScript/Python)
• Proven experience designing, building, and scaling Data QA processes
• Experience managing QA for projects and working in standard QA and project management tools like Testrails, Jira, Confluence
• Experience with white/grey box testing.
• Comfortable building, supporting, and integrating with production-grade ETL/data pipelines
• Build and maintain data quality automation frameworks using SQL, JavaScript/Python, or test platforms like DBT tests, Apache Airflow validations, etc.
• Understanding of the data governance concepts and experience with data governance tools or platforms.
• Understanding of cloud infrastructure (AWS preferred), CI/CD workflows, and containerization (Docker, Kubernetes)

Business Skills

• Capable of leading Data QA projects across both application and data-focused initiatives
• Strong communication skills with an ability to bridge technical and non-technical stakeholders
• Demonstrated experience mentoring QA analysts and QA engineers and contributing to QA culture and growth
• Experience in fast-paced startup or scaling environments
• Prior experience in healthcare, life sciences, or regulated data environments is a plus

Bonus Points

• Experience building or supporting admin tools and internal platforms
• Experience with data observability, schema governance, or data lineage tooling
• Exposure to machine learning pipelines, business intelligence tools, or product instrumentation
• Familiarity with mobile development frameworks such as React Native or Capacitor

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