• Own end-to-end QA delivery for a client deployment, including planning, execution, and release readiness • Lead a distributed QA/SDET team across the US, Colombia, and India — including task assignment, prioritization, and technical guidance • Define and drive an automation-first test strategy while ensuring effective manual testing where needed • Track quality metrics, identify risks, and communicate status clearly to stakeholders and leadership • Collaborate closely with developers, product managers, release managers, and internal/external teams across time zones during each release • Support and collaborate with client test teams to ensure successful and timely completion of UAT and other processes leading up to release • Continuously improve test frameworks, tooling, and QA processes across the organization • Analyze requirements and workflows to design effective test coverage spanning both manual and automated testing • Actively contribute to manual testing for new features, edge cases, and exploratory scenarios • Write and review automated tests across API and integration layers • Identify, document, and triage defects with clear reproduction steps and root cause insights • Design, develop, and maintain test automation frameworks and tooling • Use AI tools to improve testing outcomes, including test generation, defect analysis, and automation acceleration • Leverage AI-assisted code generation and debugging tools to increase team velocity • Define evaluation and validation approaches for AI/ML-powered features within the product • Integrate testing into CI/CD pipelines to enable continuous testing and fast feedback loops • Champion shift-left testing practices and developer-owned quality initiatives • Maintain and evolve pipeline configurations to support parallel execution and automated quality gates

• Design and implement a data quality framework across Bronze, Silver, and Gold layers — defining validation rules, threshold tolerances, and alerting standards. • Build and maintain automated data quality checks within Databricks pipelines — row counts, null checks, referential integrity, schema validation, and business rule assertions. • Own reconciliation between source systems and Databricks layers — ensuring source data lands accurately and transformations produce expected outputs. • Validate identity resolution outputs in the Silver layer — reviewing match rates, investigating false positives and false negatives, and ensuring enterprise identifiers are being assigned correctly across source populations. • Perform end-to-end pipeline testing — validating that data flows correctly from ingestion through to the Gold layer and that downstream reporting outputs reflect accurate data. • Partner with Data Engineers to define acceptance criteria for each sprint’s pipeline and data model deliverables before they are promoted to production. • Support UAT with client business stakeholders — helping them validate that Gold layer outputs meet their reporting requirements. • Document all QA processes, test results, and data quality findings in a format that can be handed off to the client team at engagement close. • Monitor pipeline health post-deployment — investigating and triaging data quality incidents and working with engineers to resolve root causes quickly.

• Ensure data quality, reliability, and consistency across modern data platforms. • Validate data pipelines and implement automated quality checks. • Collaborate closely with Data Engineering and business teams to guarantee accurate and production-ready data assets. • Design and implement a data quality framework across Bronze, Silver, and Gold layers. • Build and maintain automated data quality checks within Databricks pipelines. • Own reconciliation between source systems and Databricks layers. • Validate identity resolution outputs in the Silver layer. • Perform end-to-end pipeline testing. • Partner with Data Engineers to define acceptance criteria for each sprint’s pipeline and data model deliverables. • Support UAT with client business stakeholders. • Document all QA processes, test results, and data quality findings. • Monitor pipeline health post-deployment.

• Ensure data quality, reliability, and consistency across modern data platforms. • Validate data pipelines and implement automated quality checks. • Collaborate closely with Data Engineering and business teams. • Design and implement a data quality framework across Bronze, Silver, and Gold layers. • Build and maintain automated data quality checks within Databricks pipelines. • Own reconciliation between source systems and Databricks layers. • Validate identity resolution outputs in the Silver layer. • Perform end-to-end pipeline testing. • Partner with Data Engineers to define acceptance criteria for each sprint. • Support UAT with client business stakeholders. • Document all QA processes, test results, and data quality findings. • Monitor pipeline health post-deployment.