Our client is currently seeking an Investment QA Lead III About the Role As a Quality Assurance Lead on our financial data engineering team, you will champion data integrity, reliability, and test automation across modern investment data warehouse environments. You will partner closely with product managers, data engineers, and quantitative analysts to build and execute end-to-end testing strategies for complex, high-throughput financial data pipelines. In this role, you will be responsible for validating mission-critical datasets, performing deep root-cause analyses on complex anomalies, and ensuring that our data platform adheres to strict institutional reporting standards. This position is ideal for a data-focused QA professional who thrives in agile delivery cycles and possesses both deep SQL expertise and strong familiarity with multi-asset financial instruments. Responsibilities Define, implement, and maintain end-to-end test strategies—including functional, integration, regression, and user acceptance testing (UAT)—for large-scale data warehouse solutions. Design, build, and execute advanced SQL validation test suites to verify complex joins, aggregations, data lineage, and schema integrity across petabyte-scale data lakes and warehouses. Reconcile upstream data feeds against reporting layers, validating BI reports and executive dashboards for business logic accuracy and mathematical precision. Embed automated data quality checks and test scripts into modern CI/CD pipelines within cloud-based DevOps environments. Manage the full defect lifecycle, performing root-cause analysis, identifying recurring data quality trends, and clearly articulating financial and business impacts to cross-functional stakeholders. Support regulatory reporting, internal controls, and compliance audits through rigorous test documentation, governance standards, and traceability matrices. Minimum Qualifications Bachelor's degree in Computer Science, Data Analytics, Finance, Information Systems, or equivalent practical experience. 5 years of experience in software quality assurance or data quality engineering, with a primary focus on data-intensive systems, databases, or data warehouses. Proven experience writing complex SQL queries for data verification, reconciliation, and performance validation on large datasets. Hands-on experience working with modern cloud data warehouse platforms (e.g., Snowflake, BigQuery, or Redshift). Domain knowledge of financial products, including fixed-income instruments (e.g., Bonds, MBS, ABS), equities, mortgage loans, or derivatives. Experience delivering projects within Agile/Scrum development methodologies using enterprise tracking tools (e.g., Jira, Azure DevOps). Preferred Qualifications Experience building or maintaining data transformation testing workflows using modern data modeling frameworks like dbt (data build tool). Working knowledge of enterprise workflow orchestration platforms (e.g., Control-M, Apache Airflow). Familiarity with scripting languages (e.g., Python) for ad-hoc data validation, automation, and test framework development. Background in investment accounting, asset management, or capital markets data domains. Excellent communication and technical writing skills, with a track record of driving cross-functional alignment and quality best practices across engineering teams. Medical, dental, and vision insurance are available to qualified candidates who meet eligibility requirements.