QA Lead - Data & Pipeline Quality

QA Lead — Data & Pipeline Quality

Employment Type: Full-Time

Location: Austin, TX

About Incedo

Incedo Inc. is a high-growth Digital, Data and AI Transformation Specialist firm headquartered in New Jersey. We are a long-term strategy execution partner for Fortune 500 enterprises, operating at the intersection of business and technology across Banking & Payments, Wealth Management, Telecom, Hi-Tech, and Life Sciences.

We are building Incedo 4.0 - an AI-native, execution-focused, founder-led organization designed for scale, speed, and long-term impact.

Incedo delivers ROI from AI @ Scale through the “Power of 3”:

• Deep domain expertise
• AI & Data capabilities
• Engineering & Operations excellence

About the Role

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.

Key Responsibilities

QA Strategy & Test Framework

• 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

Financial Data Validation & Reconciliation QA

• 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

Pipeline & Integration Testing

• 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

AI-Augmented QA

• 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

Cross-Functional Collaboration & Leadership

• 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

Key Competencies

• Financial Data Fluency — You understand what positions, transactions, and reconciliation breaks mean to the business and why accuracy is non-negotiable
• QA Ownership — You don't just find bugs; you build the systems and culture that prevent them from reaching production
• AI-Forward Mindset — You actively use AI tools as force multipliers for test coverage, anomaly detection, and documentation
• Attention to Detail — You are methodical, precise, and deeply skeptical of data that looks off
• Cross-Functional Influence — You can work across engineering, operations, and service teams to champion data quality without direct authority