QA Lead - Data & Pipeline Quality

Key Responsibilities

• Own and evolve the end-to-end QA strategy for data pipelines, ETL/ELT workflows, and financial data integrations
• Design and implement scalable test frameworks covering data validation, schema integrity, transformation accuracy, and business rule compliance
• Define QA standards, best practices, and documentation requirements for the data engineering team
• Lead test planning, test case design, and execution across new pipeline builds and platform changes
• Validate the accuracy and completeness of wealth management datasets including positions, transactions, accounts, clients, advisors, and security master data
• Design and run reconciliation QA processes to surface breaks between custodians, internal systems, and third-party data providers
• Build automated data quality checks, threshold alerts, and validation rules to catch issues before they reach advisors or clients
• Investigate and document root causes of data quality failures and partner with engineering to drive permanent fixes
• Lead QA efforts across data ingestion, transformation, and delivery layers within the Microsoft Azure and Databricks environment
• Design regression test suites to ensure pipeline changes don't introduce data quality regressions
• Collaborate with data engineers during development to shift quality left — embedding QA checkpoints earlier in the build cycle
• Validate data outputs against business requirements and financial data specifications
• Actively leverage AI tools (e.g., GitHub Copilot, Claude, ChatGPT) to accelerate test case generation, anomaly detection, and QA documentation
• Identify opportunities to apply AI/ML techniques to data quality problems such as automated break detection, outlier identification, or pattern-based validation
• Champion an AI-forward approach to QA across the team and bring practical recommendations for tooling improvements
• Partner with data engineering, operations, and service teams to align on data quality standards and resolution workflows
• Serve as the QA voice in sprint planning, pipeline design reviews, and platform release cycles
• Mentor junior QA team members and help build a quality-first culture across the data organization

Required Qualifications

• 5–8 years of experience in data quality, QA engineering, or data testing, with direct exposure to wealth management data domains
• Hands-on experience validating wealth management datasets including positions, transactions, accounts, clients, advisors, and security master data
• Experience designing and executing reconciliation QA processes across custodians, platforms, or internal financial systems
• Proficiency with SQL and at least one scripting language (Python preferred) for building automated data validation and testing workflows
• Experience working within Microsoft Azure cloud environments (Azure Data Factory, Azure Data Lake, or equivalent)
• Strong understanding of ETL/ELT pipeline architecture and the ability to test at each layer of a data pipeline
• Demonstrated use of AI tools in day-to-day QA work — we expect QA leads to be actively leveraging AI to improve coverage and efficiency
• Strong documentation skills — test plans, data quality runbooks, and root cause analyses should be second nature

Preferred Qualifications

• Experience with Databricks or PySpark in a testing or validation context
• Familiarity with Delta Lake, Unity Catalog, or data lakehouse quality frameworks
• Exposure to custodial data feeds and formats (Schwab, Fidelity, Pershing, or similar)
• Experience with advisor technology platforms such as Addepar, Black Diamond, Envestnet, Orion, or Tamarac
• Knowledge of financial instruments including equities, fixed income, alternatives, and managed accounts
• Familiarity with data observability tools (e.g., Monte Carlo, Great Expectations, dbt tests)
• Experience in a fintech, WealthTech, RIA, or asset management environment