Transform healthcare with us.

At Qualified Health, we're redefining what's possible with Generative AI in healthcare. Our infrastructure provides the guardrails for safe AI governance, healthcare-specific agent creation, and real-time algorithm monitoring, working alongside leading health systems to drive real change.

This is more than just a job. It's an opportunity to build the future of AI in healthcare, solve complex challenges, and make a lasting impact on patient care. If you're ambitious, innovative, and ready to move fast, we'd love to have you on board.

Job Summary

We are hiring a Senior Staff Engineer to set the technical direction for the Data Platform and to build it hands-on. Databricks is the center of the role: you will own the architecture of our lakehouse and the data pipelines, governance, and performance that run on it. In addition, you will build and run the AI pipelines that sit alongside it, using Temporal for durable orchestration. You will work across product, AI, and engineering pods, and you will raise the bar for how the whole team builds, including how we use AI coding agents to ship faster and with higher quality.

You set platform-wide strategy and architecture across multiple teams. You anticipate problems a year or more out, shape the roadmap with leadership, and measurably raise engineering effectiveness across the organization, while staying close enough to the code to build the hardest parts yourself.

Key Responsibilities

Own the lakehouse. Architect and evolve our Databricks lakehouse, including medallion layering (bronze, silver, gold), Delta Lake, Unity Catalog governance, and workspace and compute strategy.

Build and tune Databricks pipelines. Design, build, and optimize Spark and SQL pipelines on Databricks, including incremental processing, data quality enforcement, performance tuning, and cost management.

Make data trustworthy. Define data contracts, quality checks, access controls, and PHI-aware governance in partnership with security and compliance.

Power AI and LLM workloads. Build the data foundations for AI products: LLM-ready datasets, retrieval and vector-store pipelines, evaluation data, and the interfaces that let AI and product teams consume governed data safely.

Build AI pipelines on Temporal. In addition to Databricks, design durable workflows on Temporal for AI pipelines: scheduled and watermarked pulls from Databricks gold tables, file-ingestion workflows, and long-running LLM extraction and enrichment jobs. Establish patterns for retries, idempotency, versioning, and backfills, and keep the handoff between Databricks and Temporal clean, observable, and well governed.

Lead agentic engineering. Use AI coding agents daily and define how the team does agentic development: tooling, prompts, guardrails, code review practices, and test strategy for agent-written code.

Set technical direction. Write and review technical designs, set platform-wide standards, and make build-versus-buy decisions that hold up over multiple years.

Multiply the team. Mentor engineers across teams, unblock other pods, and communicate trade-offs clearly to technical and non-technical stakeholders.

Required Qualifications

Bachelors Degree in Computer Science, Electrical Engineering, Mathematics or a related field

At least 12+ years of software or data engineering experience, with a track record of designing and operating production data platforms at scale and of setting architecture across multiple teams.

Experience working in a highly regulated domain (Healthcare, Insurance, Finance) is required

Deep, hands-on expertise with Databricks is highly preferred but expertise in Snowflake or Microsoft Fabric would be aligned as well.

Experience with the lakehouse stack: Spark, Delta Lake, Unity Catalog, Workflows or equivalent, and performance and cost tuning at scale.

Experience building pipelines for ML or LLM applications, such as LLM-based extraction, embeddings and vector search, feature pipelines, or evaluation datasets.

Fluency in Python and SQL, with sound software engineering practices: testing, CI/CD, infrastructure as code, and code review.