Senior Data Engineer (Data + Applied AI)

Building and maintaining production-grade data pipelines in cloud data warehouses such as Google BigQuery or equivalent, following architectural standards set by the Director of Data and AI.

Designing and developing dbt models across bronze, silver, and gold layers, including a focus on quality and governance via automated tests, documentation, and incremental load strategies.

Creating and optimizing Airflow DAGs for data workflow orchestration, including scheduling, dependency management, error handling, and alerting.

Implement dimensional data models and data mart structures — guided by the team's modeling standards — that support clinical BI and ML feature consumption.

Crafting easy-to-understand visualizations and dashboards that align with commonly used business analytic standards in Looker or equivalent BI tools in close collaboration with product analytics, finance, operations, growth, and clinical stakeholders.

Integrating healthcare data from sources such as EHRs, Stripe, 3rd-party APIs, and application database feeds, normalizing incoming data into the unified data platform.

Applying HIPAA-compliant data handling practices, including PHI/PII masking, tokenization, audit logging, and role-based access controls across all pipeline and AI system work.

Architecting and implementing RAG pipelines — including document ingestion, chunking, embedding generation, and retrieval — using frameworks such as LangChain or LangGraph

Supporting MLOps workflows, including model training pipeline maintenance, deployment support, performance monitoring, and retraining triggers

Code reviewing PRs from teammates, providing constructive technical feedback to peers, and upholding the team's engineering standards.

Collaborating closely with product managers to understand requirements and deliver reliable data and AI products.

Monitoring and triaging assigned pipeline and data quality failures, escalating architectural issues as appropriate.

Documenting pipeline designs, data models, and technical decisions in alignment with the team's governance and lineage tracking standards.

Evaluating new tools and frameworks, providing hands-on prototyping and technical assessments.