Role Description The Senior AI Data Engineer will serve as both architect and builder of our data ecosystem. Every initiative will follow a complete engineering lifecycle: - Gathering stakeholder requirements - Designing the solution - Building and testing it - Shipping it to production This role will work across data lakes, analytics pipelines, and lightweight application development— the multi-disciplinary data equivalent of a full-stack developer. The Senior AI Data Engineer will work closely with the data science, finance, and clinical operations teams to design intelligent, automated data solutions that power care decisions, financial planning, and operational efficiency. AI augmentation is not optional — it is the standard working mode. Qualifications - 5+ years of hands-on data engineering experience, including building and operating production data pipelines. - Expert-level Python skills for ETL, pipeline orchestration, and automation. - Deep SQL proficiency — query optimization, data modeling, stored procedures. - 2+ years’ experience working with AI first development workflows. - 4+ years’ experience with AWS (S3, Glue, Lambda, Redshift) and/or Azure big data services. - 1+ year of experience with Snowflake. - 2+ years of experience with orchestration frameworks. - 2+ years of Salesforce experience with Apex and configurations. - Experience with Kimball dimensional modeling — building star schemas and conformed dimensions in production. - Power BI (or equivalent BI tool) experience — data model design and report development. - API integration experience — REST, GraphQL, event streaming (Kafka, Kinesis, or similar). - Application development literacy — comfortable building lightweight web tooling (Python/Flask, Node, or similar) to complement data products. - Reside in one of the specified states. Requirements - Engage stakeholders directly to gather, clarify, and document project requirements. - Translate requirements into architected data solutions: choose the right storage, pipeline, modeling, and delivery approach for each problem. - Own testing end-to-end — unit tests, data quality checks, reconciliation, and integration tests before anything reaches production. - Deploy solutions to production and monitor post-deployment health, iterating rapidly based on real-world feedback. - Run parallel AI coding sessions across different facets of a pipeline simultaneously — orchestrate, verify, and integrate the outputs. - Build and maintain context files for data projects that encode schema conventions, pipeline patterns, and institutional knowledge. - Design verification loops: automated data quality checks, dbt tests, CI hooks, and pipeline monitors. - Build MCP (Model Context Protocol) or equivalent integrations to connect AI agents directly to various platforms. - Design and build complex, reliable data pipelines ingesting from multiple sources into our AWS Data Lake and Snowflake warehouse. - Implement and evolve data models using Kimball methodology to support analytics. - Optimize pipeline performance, manage data quality, and perform root-cause analysis on data anomalies. - Develop and maintain orchestration workflows in Python and AWS Glue. - Continuously evolve the data schema as business and engineering requirements change. - Build and support Power BI data models and reports; empower analytics team members to self-serve on a reliable data foundation. - Work with data analysts and data scientists to build reusable, well-documented pipeline components. - Deliver data products that drive clinical care decisions, financial planning, and operational performance improvements. - Build lightweight internal data applications and tooling where needed. - Design for agentic workflows: build AI-powered data tools accessible via web interfaces or Slack. - Ensure data security and HIPAA compliance in all pipeline and application work. - Document decisions, tradeoffs, and architecture clearly. - Collaborate across IT, finance, clinical operations, and data science. Benefits - Medical, dental, and vision insurance. - 401(k) plan with company matching and rapid vesting. - Paid holidays and wellness days. - Life insurance and disability insurance options. - Tuition reimbursements for professional development and continuing education. - Referral bonuses.