QA Lead - Data & Pipeline Quality
On-site · Austin, Texas, United States
Job Summary
QA Lead for data and pipeline quality on a wealth-management-focused platform. Lead end-to-end QA strategy for data pipelines (ETL/ELT), data validation, schema integrity, transformation accuracy, and business-rule compliance. Design and implement scalable automated data quality checks, reconciliation processes across custodians/internal systems, and AI-augmented QA methods to accelerate test case generation and anomaly detection. Collaborate with data engineering, operations, and service teams; mentor junior QA members; drive quality-left practices in Azure-based data environments (Azure Data Factory/Data Lake, Databricks) with a focus on accuracy of positions, transactions, accounts, clients, advisors, and security master data. Proficient in SQL and Python for automated validation, with experience validating wealth-management datasets and data outputs against business requirements and financial data specifications.
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 ability to test at each layer of a data pipeline
- Demonstrated use of AI tools in day-to-day QA work — actively leveraging AI to improve coverage and efficiency
- Strong documentation skills — test plans, data quality runbooks, and root cause analyses
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