Our hiring partner is building the technology infrastructure the nation needs to deliver the healthcare everyone deserves. As a health technology company providing benefit administration solutions to employers, unions, health plans, and government entities, they are replacing fragmented, outdated systems with an industry-first unified claims processing architecture. This platform seamlessly consolidates pharmacy and medical benefit administration onto a single, secure system - delivering true price transparency, eliminating unnecessary middleman fees, and leveraging advanced AI-powered care delivery.

The company is the intelligence platform powering benefits plans for millions of Americans and is proudly leading the next generation of care. Their data organization is central to this mission, building the trusted, well-documented datasets that power analytics, products, and decision-making across the enterprise.

Why This is a High-Impact Opportunity

1. Build Trusted Data Foundations: Design and maintain reliable data models within Snowflake using dbt, directly powering analytics and products that impact healthcare outcomes.
2. Drive Data Quality & Observability: Apply and improve data quality, testing, observability, and lineage standards across a modern, modular data platform.
3. Collaborate Cross-Functionally: Partner with analysts, data scientists, and software engineers to define data contracts and deliver well-documented, scalable datasets.
4. Mission-Driven Work: Join a collaborative, high-growth environment where your data engineering work directly contributes to transforming the American healthcare experience.

About the Role

We are seeking a Data Engineer II (Analytics Engineer) to join the mission to transform pharmacy benefits and healthcare navigation. You will build and maintain reliable data models within Snowflake with dbt, and improve data quality and observability. You will partner with analysts, data scientists, and software engineers to deliver trusted, well-documented datasets that power analytics and products.

This role sits at the intersection of data engineering and analytics, requiring strong SQL and Python skills, hands-on dbt experience, and a commitment to building scalable, maintainable data infrastructure.

Key Responsibilities

1. Build, test, and document Snowflake data models and business logic in dbt.
2. Apply and improve data quality, testing, observability, and lineage standards.
3. Collaborate with cross-functional partners to define data contracts and interfaces.
4. Contribute to a modular data platform and client-specific data configurations.
5. Participate in design reviews; propose scalable, maintainable patterns.
6. Monitor pipeline health, troubleshoot incidents, and drive root-cause fixes.
7. Optimize cost and performance of jobs, storage, and queries with guidance.
8. Write clear documentation and support knowledge sharing.

Preferred Qualifications

1. 3+ years in data engineering, software engineering, or a related field.
2. Strong SQL and Python; solid understanding of ETL/ELT and dimensional/data vault modeling.
3. Hands-on dbt experience (models, tests, documentation, deployments).
4. Experience with orchestration (Dagster or Airflow) and scheduling best practices.
5. Familiarity with Spark/Databricks and cloud data warehouses (Snowflake preferred).
6. CI/CD for data (Git-based workflows, environments) and basic IaC familiarity.
7. Data observability and testing (dbt tests, lineage tools).
8. Understanding of security, governance, and privacy for PII/PHI (HIPAA awareness).
9. Effective communication and collaboration with technical and non-technical stakeholders.
10. Bonus: Exposure to healthcare data (claims, eligibility, provider directories).
11. Bonus: Performance tuning in Snowflake (warehouse sizing, clustering, caching).

Apply Now

If you are a data engineer with strong SQL and dbt expertise and a passion for building reliable, well-documented data models in a mission-driven healthcare environment, we encourage you to apply. This is a defining opportunity to build the data foundations that power analytics and products at scale.