| Enterprise Data Platform Lead QA QE with Big Query || REMOTE at Enterprise, Utah, USA |
| Email: [email protected] |
|
http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=3422261&uid=fa3a9338095046919a774a163b02e80b From: Sudhansu Nayak, Tek inspirations [email protected] Reply to: [email protected] Job Description - Enterprise Data Platform Lead QA/QE with Big Query REMOTE need at-lest 10 year of candidate dob before 1991 also must have old linekden with good connections same title on lieninkden must have lienkden profile est /cst Must have Retail Supply Chain experience Must have GCP / BigQuery experience Only Job Summary The EDP QA / QE Lead owns the enterpriselevel 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 multiwave migration from an onpremises Enterprise Data Warehouse (EDW) to a modern cloud data platform. This is a datacentric 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 programs 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 steadystate operations. Develop and maintain the Data Quality framework and master test plan aligned to the program roadmap and release schedule. Define sourcetotarget reconciliation strategy across legacy EDW, source systems, and the new EDP (row counts, aggregates, fieldlevel validation, checksums). Define migration testing approaches including parallelrun validation, historical data validation, cutover testing, and postcutover stabilization. Define required testing levels across the data lifecycle: unit, integration, endtoend, 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 preprod. 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 signoff. 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 largescale 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 rootcause analysis. Handson 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 multiwave 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 businessrisk language. Bachelors degree in a related field or equivalent experience. Preferred Qualifications Experience leading QA for an onprem EDW to cloud data platform migration. Handson 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 handson delivery capability. Strong ownership mindset and accountability for endtoend data quality. Curiosity about modern data engineering, observability, and AI in quality. Coachingoriented leader who develops QA engineers. Clear, concise communicator able to tailor messaging to technical and executive audiences. Sudhansu Nayak Associate Technical Recruiter E: [email protected] TEK Inspirations LLC | 13573 Tabasco Cat Trail, Frisco, TX 75035 Keywords: continuous integration continuous deployment quality analyst artificial intelligence user interface business intelligence information technology Texas Enterprise Data Platform Lead QA QE with Big Query || REMOTE [email protected] http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=3422261&uid=fa3a9338095046919a774a163b02e80b |
| [email protected] View All |
| 01:48 AM 04-Jun-26 |