Senior Data Engineer

We are a healthcare technology company that provides platforms and solutions to improve the management and access of cost‑effective pharmacy benefits. Our technology helps enterprise and partnership clients simplify their businesses and helps consumers save on prescriptions.

As a leader in SaaS technology for healthcare, we offer innovative solutions with integrated intelligence on a single enterprise platform that connects the pharmacy ecosystem. With our expertise and modern, modular platform, our partners use real‑time data to transform their business performance and optimize their innovative models in the marketplace.

Position Summary

The Senior Data Engineer owns the data platform end to end — designing, building, and maintaining the infrastructure, pipelines, and governed data layers that power analytics and downstream consumers across the organization. This is a hands‑on engineering role centered on cloud data platform development, pipeline orchestration, and data modeling at scale.

The guiding principle is to build once, consume everywhere — define transformations and metrics a single time, governed and version‑controlled in the warehouse, then serve them consistently to every downstream consumer: BI tools, client‑facing reporting, and AI systems.

The ideal candidate is comfortable building data platforms and systems from scratch, thrive in a platform development and maintenance environment, and brings strong hands‑on experience with Snowflake and workflow orchestration tools such as Apache Airflow and dbt (or equivalent platforms). This role does not own BI visualization — your work spans raw ingestion through the governed gold layer.

Key Responsibilities

Data Platform Development & Maintenance

• Design, build, and maintain scalable data pipelines and platform infrastructure on Snowflake.
• Architect and implement end‑to‑end data flows from source ingestion through the silver and gold layers.
• Own platform reliability, performance tuning, and ongoing maintenance — treating the data platform as a product.
• Build systems from the ground up where needed; evaluate existing systems and refactor for scale, cost efficiency, and maintainability.
• Establish engineering standards: version control, CI/CD, testing frameworks, and documentation for all platform components.

Pipeline Orchestration & Workflow Management

• Design and maintain orchestration workflows using Apache Airflow (or equivalent) to schedule, monitor, and manage data pipelines.
• Ensure pipeline reliability, observability, and failure recovery through alerting, logging, and automated retries.
• Coordinate dependencies across upstream sources and downstream consumers with well‑defined SLAs.

Gold‑Layer Modeling & Metrics

• Design, build, and maintain gold‑layer data models and a governed metrics layer in Snowflake using dbt — clean, tested, documented, and version‑controlled.
• Establish metric definitions as the single source of truth: calculated once, reused everywhere, with clear ownership and change management.
• Push transformation and calculation logic upstream into the warehouse so the consumption layer stays simple, consistent, and high‑trust.
• Partner with analytics and BI stakeholders on the boundary between pipelines (data engineering) and consumption (analytics/reporting).
• Structure and document data models so they meet the governance and consistency requirements of AI agents and autonomous analytical systems.

AI‑Native Development

• Build with coding agents and AI‑assisted tooling as a primary part of the workflow — accelerating pipeline development, data modeling, documentation, and testing.
• Independently set up and manage development environments, repositories, and the tooling needed to work effectively with agentic systems.
• Help establish team…