Design, build, and maintain scalable data ingestion and egress pipelines using Airflow and dbt Cloud. Write Python for Airflow DAGs and utilities, model and transform healthcare datasets into analytics-ready formats with SQL and dbt, implement tests and monitoring, and collaborate with cross-functional teams to support data science and reporting needs.
Empassion is a Management Services Organization (MSO) focused on improving the quality of care and costs on an often neglected “Advanced illness/ end of life” patient population, representing 4 percent of the Medicare population but 25 percent of its costs. The impact is driven deeper by families who are left with minimal options and decreased time with their loved ones. Empassion enables increased access to tech-enabled proactive care while delivering superior outcomes for patients, their communities, the healthcare system, families, and society.
What success looks like
- Partner with teams across the business and external partners to understand data needs and deliver reliable pipelines and models that solve real problems.
- Build and maintain scalable ingestion and egress pipelines in Airflow and dbt Cloud, ensuring high quality, automated data flows across cloud environments.
- Implement unit tests and monitoring to guarantee data integrity and reproducibility.
- Model and transform and structure healthcare datasets into usable formats that power data science models, and other reporting marts
- Enhance and scale data models with SQL and dbt, ensuring precision and adaptability for new partnerships.
- Write Python code for Apache Airflow DAGs, components and utilities that orchestrate and monitor data workflows. Build complex pipelines that enable flexible scheduling, conditional logic, and smooth integration across multiple data sources.
What you will bring
- 2+ years in data engineering or analytics engineering with proven ability to build pipelines and scalable workflows.
- Strong SQL skills for querying large, complex datasets.
- Proficiency in Python for data engineering tasks (transformations, APIs, automation).
- Experience with cloud data warehouses and storage (GCP preferred: BigQuery, Cloud Storage, Composer; AWS/Azure equivalents acceptable).
- Hands on experience with dbt or similar data modeling tools.
- Comfort working in collaborative dev/staging/prod environments, partnering with Product and Tech to safely test, launch, and anticipate the impact of new changes.
- Curiosity about operational workflows and a drive to partner with non-technical teams, ensuring data and reporting align with how the business actually runs. You're not just a spec-taker, you're part of the solution.
- A proactive, problem-solving mindset and ability to thrive in fast-paced, iterative environments.
- Strong communication skills to collaborate with analysts, engineers, and business stakeholders.
- Knowledge of healthcare data (claims, ADT feeds, eligibility files).
- Familiarity with Git/GitHub for version control.
- Early-stage startup experience (seed/Series A), especially mission-driven ones.
- Experience building semantic layers and data models in Looker (LookML).
Nice to have:
Ready to Make a Difference?
If you’re driven by data, healthcare, and impact, apply and let’s talk!
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