QA Lead - Data & Pipeline Quality

Qode•Arlington, TX

7h•Onsite

About The Position

We are seeking an experienced QA Lead to own data and pipeline quality across our wealth management technology platform. This is a critical role responsible for ensuring the integrity, accuracy, and reliability of the financial data that advisors, clients, and operations teams depend on every day. The ideal candidate has a strong wealth management background and understands what's at stake when data is wrong — whether that's a position break, a misallocated transaction, or a stale security price. You will design and lead QA frameworks, own test strategy for data pipelines, and serve as the last line of defense before bad data reaches downstream consumers. You are also expected to actively leverage AI tooling to improve coverage, speed, and the quality of your team's output.

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

Nice To Haves

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

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