QA Lead – Data & Pipeline Quality

Jobtailor; Austin (TX); On-site; USD 110,000 - 160,000; Full time

Job summary

Jobtailor is seeking a Senior QA Engineer focused on data quality for wealth management data pipelines in Austin. You will own the end-to-end QA strategy, design scalable test frameworks, and collaborate with data engineers to ensure data accuracy across ingestion, transformation, and delivery layers in Azure and Databricks.

The role requires 5–8 years in data quality or QA engineering with SQL and Python experience, plus hands-on validation of wealth management datasets.

Qualifications

• 5–8 years of experience in data quality, QA engineering, or data testing in wealth management contexts.
• 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 Python for building automated data validation and testing workflows.
• Experience working with Microsoft Azure cloud environments (Azure Data Factory, Azure Data Lake, or equivalent).
• Demonstrated use of AI tools in day-to-day QA work to improve coverage and efficiency.
• Strong documentation skills for test plans and runbooks, plus root cause analyses.

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 regression test suites to prevent data quality regressions during pipeline changes.
• Collaborate with data engineers to shift quality left in the build cycle and embed QA checkpoints earlier.

Skills

SQL

Python

Azure

Databricks

Data quality

QA testing

AI tools

Documentation

Tools

Azure Data Factory

Azure Data Lake

Databricks

Job description

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 (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

Requirements

• 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.