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We are seeking a Senior Test Lead with a deep specialization in Data Engineering QA and Databricks to oversee quality assurance for large-scale enterprise data transformation projects. This role is pivotal in ensuring the integrity of complex data pipelines, from ingestion to warehousing. You will lead the development of comprehensive test strategies, promote "shift-left" testing methodologies, and direct the validation of distributed systems. The ideal candidate will bridge the gap between technical data engineering and executive stakeholder reporting, ensuring high standards of data governance and compliance.
Job Responsibility:
Strategic Data Testing: Lead the design and development of end-to-end test strategies specifically tailored for data projects, including Databricks pipeline testing and CDC (Change Data Capture) validation
Technical Validation: Perform deep-dive inspections of Parquet files and validate complex FHIR JSON structures to ensure interoperability and data accuracy
Pipeline Automation: Leads the architectural design and maintenance of automated test frameworks for ETL/pipelines using Databricks notebooks and industry-standard tools like Selenium
Data Governance & Integrity: Act as a quality advocate by implementing data profiling and data quality assessments
ensure rigorous adherence to data lineage, compliance, and lineage standards
Risk Management: Proactively identify and mitigate quality-related risks in distributed systems, collaborating with architects to embed security testing into the lifecycle
Team Leadership: Mentor and coach a team of QA professionals, fostering a culture of continuous learning and high performance within an Agile environment
Stakeholder Liaison: Develop and implement data quality metrics and KPIs, providing clear status reports and summary documentation to project leadership and stakeholders
Requirements:
Databricks Expertise: Extensive experience in Databricks pipeline testing, notebook-based automation, and Parquet/JSON inspection
Advanced SQL: Mastery of SQL for querying large datasets, performing complex joins, and verifying relational database integrity (Oracle, SQL Server)
Data Engineering QA: Proven track record in CDC validation and verifying structured/unstructured data formats (XML, FHIR JSON)
Warehousing Concepts: Strong background in Data Modeling, Data Warehousing methodologies, and Data Governance standards
Tooling Proficiency: Expert-level experience with Azure DevOps for test management and defect tracking
Compliance Knowledge: Understanding of AODA testing requirements and security best practices for sensitive data handling
Seniority: 10+ years of experience in the full SDLC, with at least 5 years in a leadership or strategic planning capacity for data-centric projects
What we offer:
Cutting-Edge Data Stack: Lead the quality vision for a modern data environment utilizing Databricks, Azure DevOps, and FHIR standards
Strategic Influence: Shape the "shift-left" culture of an organization, moving beyond simple execution to strategic quality architecture
High-Impact Projects: Oversee the data integrity for massive enterprise transformations that directly influence operational feasibility and decision-making
Professional Mentorship: Play a defining role in upskilling a team of testers in modern data engineering QA techniques