Enterprise Data Platform Lead QA/QE with Big Query experience

Remote EST/CST

Key Responsibilities

·  Own the enterprise quality engineering strategy across migration waves, new builds, and steady state operations.

·  Develop and maintain the Data Quality framework and master test plan aligned to the program roadmap and release schedule.

·  Define source to target reconciliation strategy across legacy EDW, source systems, and the new EDP (row counts, aggregates, field level validation, checksums).

·  Define migration testing approaches including parallel run validation, historical data validation, cutover testing, and post cutover stabilization.

·  Define required testing levels across the data lifecycle: unit, integration, end-to-end, regression, performance, and UAT.

·  Establish data quality dimensions for every pipeline (completeness, accuracy, consistency, timeliness, validity, uniqueness, referential integrity).

·  Define performance, scalability, and resilience testing for data pipelines and consumption layers.

·  Select and govern the QA tooling stack (e.g., dbt tests, Great Expectations, Soda, Datafold, data observability platforms).

·  Drive automation strategy and integrate automated data tests into CI/CD pipelines.

·  Define test environment and test data management standards across dev, QA, UAT, and pre-prod.

·  Build and maintain a centralized library of reusable test cases, data sets, and validation rules.

·  Identify opportunities to leverage AI / GenAI for QA acceleration (test generation, anomaly detection, reporting).

·  Manage and mentor QA / QE resources across distributed teams and vendor models.

·  Plan, coordinate, and lead UAT, including scenario design, SME enablement, environment readiness, execution support, and sign off.

·  Define and run the defect lifecycle and chair quality/defect review forums.

·  Define release quality gates and entry/exit criteria; sign off on release readiness.

·  Establish and publish quality KPIs (defect density, escape rate, automation coverage, data quality scores, reconciliation pass rates).

Required Qualifications

·  12+ years of progressive QA / Quality Engineering experience, including 3+ years in a lead/principal role.

·  Proven experience leading QA for large scale data platform, data warehouse, or data lake/lakehouse programs.

·  Deep expertise in data testing: ETL/ELT validation, reconciliation, schema validation, transformation testing, historical parity.

·  Strong SQL skills for complex validation and root cause analysis.

·  Hands on experience with at least one modern cloud data platform (Snowflake, Databricks, Azure Synapse/Fabric, BigQuery, Redshift).

·  Experience with modern data testing frameworks (dbt tests, Great Expectations, Soda, Datafold, etc.).

·  Experience defining QA strategy, master test plans, quality gates, and entry/exit criteria for multi wave programs.

·  Experience managing distributed QA teams across onshore/offshore/vendor models.

·  Working knowledge of CI/CD, version control, and DevOps/DataOps practices.

·  Proven experience planning and running UAT.

·  Strong communication skills with the ability to translate technical quality concerns into business risk language.

·  Bachelor’s degree in a related field or equivalent experience.

Preferred Qualifications

·  Experience leading QA for an on prem EDW to cloud data platform migration.

·  Hands on experience using AI / GenAI for QA activities.

·  Experience with data observability platforms (Monte Carlo, Bigeye, Acceldata, etc.).

·  Familiarity with data governance, lineage, and cataloging tools (Collibra, Alation, Atlan).

·  Working proficiency in Python or similar scripting languages.

·  Experience validating BI/reporting layers and semantic models (Power BI, Tableau, Looker).

Team & Culture Fit

·  Strategic thinker with hands on delivery capability.

·  Strong ownership mindset and accountability for end-to-end data quality.

·  Curiosity about modern data engineering, observability, and AI in quality.

·  Coaching oriented leader who develops QA engineers.

·  Clear, concise communicator able to tailor messaging to technical and executive audiences.