Data Quality Assurance (QA) & Testing Manager

About the role

We are seeking a hands-on Data Quality Assurance (QA) & Testing Manager to lead testing strategy, execution, automation, and quality controls for enterprise data ingestion, integration, transformation, storage, and consumption. This leader will ensure that data is accurate, complete, reliable, secure, traceable, and fit for analytics, modeling, AI, and operational use.

Data QA Strategy & Leadership

• Own data platform and integrations QA strategy, testing standards, planning, quality gates, defect management, and release-readiness recommendations.
• Lead and coach QA analysts and testing engineers; manage priorities, capacity, stakeholder expectations, and delivery risk.
• Partner with data engineering, integration, domain leads, architecture, security, and governance teams to shift quality controls earlier in delivery.

SQL Validation & Reconciliation

• Design and execute advanced SQL validation of source-to-target mappings, joins, transformations, aggregates, business rules, and referential integrity.
• Apply repeatable data validation and reconciliation practices, maintaining test cases, results, exceptions, evidence, and approvals.
• Validate record counts, completeness, duplicates, incremental loads, schema changes, and downstream consistency; investigate discrepancies to root cause.

ETL, API & Integration Testing

• Lead end-to-end testing for all solutions and changes delivered by the Integrations, RTE Development, and Platform teams, including Talend ETL/ELT workflows, MuleSoft and SOA services, REST/SOAP APIs, file-based ingestion, and point-to-point connections.
• Validate authentication, payloads, contracts, file layouts, scheduling, retries, error handling, recovery, and upstream/downstream dependencies.
• Coordinate integration, regression, performance-relevant, and release testing across batch and near-real-time data flows.

SQL/Python, Automation & AI-Assisted Testing

• Build and maintain Python/SQL automation for reconciliation, transformation checks, API tests, schema validation, and repeatable regression suites.
• Integrate automated checks into CI/CD pipelines and establish actionable reporting of failures, coverage, and quality trends.
• Use enterprise-approved AI-assisted tools to accelerate test design, SQL/Python script development, defect triage, and anomaly investigation.
• Evaluate AI-generated test artifacts for accuracy, coverage, security, and reproducibility; require human review and documented validation for controlled releases.

Data Platforms, Governance & Compliance

• Validate cloud data ingestion and transformations across enterprise platforms; Snowflake and Databricks knowledge is preferred.
• Ensure testing supports lineage, traceability, access controls, sensitive-data protection, change management, and audit readiness.
• Maintain evidence of testing, defects, remediation, approvals, and release decisions in accordance with applicable enterprise and regulatory controls.