QE Lead

Location: Freeport

Job Summary

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

This is a data-centric quality role. The focus is on data validation - reconciliation, parity, transformation correctness, and data quality across pipelines - rather than traditional system or UI functional testing. The Lead defines the program Data Quality framework and test plan aligned to the EDP roadmap waves and release schedule, covering automated testing, migration validation, performance testing, UAT, and release quality gates.

The role also manages a team of QA / QE resources embedded across implementation work streams who execute test cases, identify and resolve defects, and prepare UAT - while this role sets the standards, tooling, environments, and metrics that govern quality across the program.

Key Responsibilities

• Own the enterprise quality engineering strategy for EDP across migration waves, new builds, and steady-state operations.
• Develop and maintain the program Data Quality framework and master test plan, aligned to the EDP roadmap, release schedule, and migration sequencing.
• Define the source-to-target reconciliation strategy between legacy EDW, source systems, and EDP - including row counts, aggregate parity, field-level validation, and checksum approaches.
• Define the migration testing approach: parallel-run validation, historical data validation, cutover testing, and post-cutover stabilization checks.
• Define the levels and types of testing required at each stage of the data lifecycle - unit, integration, end-to-end, regression, performance, and UAT - and the data quality dimensions to be validated on every pipeline (completeness, accuracy, consistency, timeliness, validity, uniqueness, referential integrity).
• Define performance, scalability, and resilience testing for data pipelines and consumption layers under representative production volumes.
• Select, standardize, and govern the QA tooling stack - including data testing frameworks (e.g., dbt tests, Great Expectations, Soda), reconciliation tooling (e.g., Datafold), and data observability platforms.
• Drive the automation strategy and integrate automated data tests into CI/CD pipelines so quality gates run on every change.
• Define test environment management and test data management standards across dev, QA, UAT, and pre-prod - including refresh, isolation, masking, and subset strategies.
• Build and maintain a centralized, reusable library of test cases, test data sets, and validation rules that can be leveraged across waves and teams.
• Identify and operationalize opportunities to leverage AI / GenAI to accelerate the QA lifecycle - including test generation, anomaly detection, and quality reporting - with appropriate human-in-the-loop validation.
• Manage and mentor a team of QA / QE resources across implementation work streams; drive consistent practices, coach engineers, and partner closely with data engineers, architects, product owners, and business stakeholders.
• Plan, coordinate, and lead User Acceptance Testing - including scenario design, business SME enablement, environment readiness, execution support, and formal sign-off.
• Define and run the defect lifecycle - intake, triage, prioritization, resolution, verification - and chair quality and defect review forums across work streams.
• Define release quality gates and entry / exit criteria, and sign off on release readiness for each wave.
• Establish and publish quality KPIs (defect density, escape rate, test and automation coverage, data quality scores, reconciliation pass rates) and communicate quality status and risks to program leadership.

Required Qualifications

• 7+ years of progressive QA / Quality Engineering experience, with 3+ years in a lead or principal role.
• Proven experience leading QA for large-scale data platform, data warehouse, or data lake / lakehouse programs.
• Deep, hands-on expertise in data testing: ETL / ELT validation, source-to-target reconciliation, schema validation, transformation testing, and historical data parity.
• Strong SQL skills, including the ability to write and reason about complex queries across large data sets for validation and root-cause analysis.
• Hands-on experience with at least one modern cloud data platform (e.g., Snowflake, Databricks, Azure Synapse / Fabric, Big Query, Redshift).
• Demonstrated experience with modern data testing frameworks such as dbt tests, Great Expectations, Soda, Datafold, or equivalent.
• Experience defining QA strategy, master test plans, quality gates, and entry / exit criteria for complex, multi-wave programs.
• Experience managing distributed QA teams across onshore / offshore and vendor…