Job Summary

The QA Lead - Data & Pipeline Quality will own data and pipeline quality across a wealth management technology platform, ensuring the integrity, accuracy, completeness, and reliability of financial data used by advisors, clients, and operations teams. The role will lead QA strategy and test frameworks for data pipelines, ETL/ELT workflows, financial data integrations, and reconciliation processes while partnering closely with data engineering and business teams. The ideal candidate must come from the financial domain and bring strong hands-on expertise in SQL scripting, basic Spark programming, QA automation processes, and the Pytest framework. The role also requires an AI-forward mindset, with active use of AI tools to improve test coverage, anomaly detection, documentation, and overall QA efficiency.

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 data engineering teams.
• 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 execute reconciliation QA processes to identify breaks between custodians, internal systems, and third-party data providers.
• Build automated data quality checks, threshold alerts, and validation rules to identify issues before they reach advisors or clients.
• Investigate and document root causes of data quality failures and partner with engineering teams to drive permanent fixes.
• Lead QA efforts across data ingestion, transformation, and delivery layers within Microsoft Azure and Databricks environments.
• Design regression test suites to ensure pipeline changes do not introduce data quality regressions.
• Collaborate with data engineers during development to shift quality left by embedding QA checkpoints earlier in the development lifecycle.
• Validate data outputs against business requirements and financial data specifications.
• Actively leverage AI tools such as GitHub Copilot, Claude, and ChatGPT to accelerate test case generation, anomaly detection, and QA documentation.
• Identify opportunities to apply AI/ML techniques to data quality challenges, including automated break detection, outlier identification, and pattern-based validation.
• Champion an AI-forward approach to QA and recommend improvements to testing tools and processes.
• 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 establish 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 or financial data domains.
• Must have financial domain experience, with strong understanding of wealth management data and business processes.
• Strong hands-on expertise in SQL scripting for data validation, reconciliation, and testing.
• Hands-on experience with basic Spark programming for data processing or validation.
• Strong understanding and practical experience with QA automation processes.
• Hands-on experience with the Pytest framework for automated testing.
• 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 Python or another scripting language for building automated data validation and testing workflows.
• Experience working within Microsoft Azure cloud environments, including Azure Data Factory, Azure Data Lake, or equivalent technologies.
• Strong understanding of ETL/ELT pipeline architecture and the ability to test data at each layer of a pipeline.
• Demonstrated experience using AI tools in day-to-day QA activities to improve testing coverage and efficiency.
• Strong documentation skills, including test plans, data quality runbooks, and root cause analyses.

Preferred Qualifications
• Experience with Databricks or PySpark in a testing or data validation context.
• Familiarity with Delta Lake, Unity Catalog, or data lakehouse quality frameworks.
• Exposure to custodial data feeds and formats such as Schwab, Fidelity, Pershing, or similar providers.
• 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 and data quality tools such as Monte Carlo, Great Expectations, or dbt tests.
• Experience in fintech, WealthTech, RIA, or asset management environments.
• Strong financial data fluency, attention to detail, QA ownership, AI-forward mindset, and cross-functional influence.