Lead Quality Assurance Engineer (USC/GC)
Role summary
We are seeking a deeply technical QA Lead to architect high-scale validation frameworks for our Healthcare R&D ecosystem. This role focuses on Databricks engineering and Autonomous AI, moving beyond traditional testing into high-complexity data forensics and agentic automation. Responsibilities include designing automated validation for Databricks ETL/ELT workflows, pioneering AI-agent testing suites using Claude and LLM frameworks, leading data reconciliation with Python and SQL, defining the QA roadmap with HIPAA/PHI compliance, and mentoring junior engineers on CI/CD infrastructure. The ideal candidate has 12+ years in QE, with 3+ years in a technical lead capacity, expert-level Databricks, Spark, Python, and SQL skills, hands-on LLM experience, and proven healthcare data model expertise.
QA Lead: Data & AI Research (Healthcare)
The Role Overview
We are seeking a
deeply technical QA Lead
to architect high-scale validation frameworks for our Healthcare R&D ecosystem. This role sits at the intersection of
Databricks engineering
and
Autonomous AI
, moving beyond traditional testing into high-complexity data forensics and agentic automation.
Core R&D Responsibilities
- Pipeline Engineering:
Design automated validation for massive
Databricks (ETL/ELT)
workflows, ensuring integrity across complex healthcare datasets (Claims, EHR).
- AI-Agent Integration:
Pioneer the use of
Claude and LLM frameworks
to build "agentic" testing suites for non-deterministic AI outputs.
- Technical Forensics:
Lead deep-dive data reconciliation and anomaly detection using advanced
Python and SQL
.
- Architectural Strategy:
Define the QA roadmap for experimental R&D models, balancing rapid prototyping with
HIPAA/PHI compliance
.
- High-Level Mentorship:
Code-review and guide junior engineers in building performant, scalable CI/CD testing infrastructure.
Technical Profile
- 12+ Years in QE:
Extensive background in backend/data-heavy environments;
3+ years
in a technical lead capacity.
- Stack Mastery:
Expert-level
Databricks, Spark, Python, and SQL
.
- AI Literacy:
Hands-on experience leveraging
LLMs (Claude/GPT)
or AI agents for intelligent automation.
- Domain Expertise:
Proven experience with
Healthcare data models
and regulatory standards.
- Mindset:
Thrives in ambiguous, high-velocity R&D labs where "standard" tools don't always apply.