Enterprise Data Platform Lead QA/QE with Big Query

Role: Enterprise Data Platform Lead QA/QE with Big Query
Location: REMOTE/ EST or CST Candidates

Must have Retail Supply Chain experience

Must have Google Cloud Platform / BigQuery experience

Job Summary

The EDP QA / QE Lead owns the enterprise?level quality engineering strategy for an Enterprise Data Platform (EDP), ensuring reliability, accuracy, performance, and trust in both migrated and newly built data products. This role is central to validating data movement, pipelines, transformations, and reconciliation against source and legacy systems across a multi?wave migration from an on?premises Enterprise Data Warehouse (EDW) to a modern cloud data platform.

This is a data?centric quality role focused on data validation — reconciliation, parity, transformation correctness, and data quality across pipelines — rather than traditional system or UI testing. The Lead defines the program’s Data Quality framework and master test plan, covering automated testing, migration validation, performance testing, UAT, and release quality gates. The role also manages QA / QE resources embedded across workstreams, setting standards, tooling, environments, and metrics that govern quality across the program.

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

7+ 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.