Staff Software Engineer, Data Engineering

Omada Health is on a mission to inspire and engage people in lifelong health, one step at a time. We rely on trusted, well‑governed data to power decisions across product, clinical, commercial, and care delivery teams.

Overview:

We are seeking a Staff Software Engineer to lead the technical strategy and implementation of our enterprise data architecture, governance foundations, and analytics enablement tooling. In this role, you will be the primary engineering counterpart to the Senior Product Manager for Data Enablement & Governance, jointly shaping the roadmap for enterprise analytics, shared definitions, and the tools that help Omada answer questions faster and more reliably.

You will design and evolve core data products, define patterns and standards used across the company, and drive the technical execution of initiatives that ensure our metrics, reports, and data products are scalable, governed, and trustworthy. This is a high‑impact, cross‑functional Staff role working across Data Engineering, Data Science, Analytics, Product, IT, and business leaders.

Key Responsibilities:

Enterprise Data Architecture

• Own the vision and technical roadmap for Omada’s enterprise data architecture, spanning ingestion, storage, modeling, and serving layers for analytics and applied statistics use cases.
• Design, implement, and evolve scalable, secure, and cost‑efficient data solutions (datalakes, warehouses, marts, semantic layers) that support governed, cross‑functional analytics and self‑service.
• Define and socialize architectural patterns, data contracts, and integration standards used by data and product teams across the organization.
• Anticipate future needs (e.g., new product lines, new modalities, AI/ML workloads) and drive proactive architectural changes rather than reacting to incidents or point‑in‑time requests.

Data Modeling, Quality, and Governance Foundations

• Lead the design of logical and physical data models to support enterprise metrics, dashboards, and ad hoc analytics, with a focus on reusability and clear ownership.
• Implement robust data quality, validation, and monitoring frameworks that underpin trusted “single source of truth” definitions for core concepts (e.g., active member, MAU, GLP‑1 member).
• Partner with the Senior Product Manager, Data Enablement & Governance to translate governance decisions (definitions, ownership, change‑management processes) into concrete technical implementations in the data platform.
• Set standards and review mechanisms to ensure new pipelines, marts, and reports align with enterprise definitions and governance policies.
• Continuously improve performance, scalability, and cost‑efficiency of data workflows and storage; lead deep dives and remediation for complex production issues.

Enterprise Data Products Lifecycle 

• In close partnership with the Senior PM, define and deliver core, reusable data products (e.g., engagement, clinical, financial, client, care delivery datasets) that power dashboards, reporting, and self‑service analytics.
• Co-Architect and implement technical foundations for AI‑assisted analytics tools, governed semantic layers, and reporting applications that make analysts and business users more efficient.
• Partner with Product and Engineering teams owning tools like Amplitude, Tableau, and internal reporting tools to ensure consistent instrumentation, mapping to enterprise definitions, and scalable access patterns.
• Translate business and product requirements into resilient schemas, data services, and interfaces that are usable, maintainable, and auditable.
• Ensure production data delivery meets defined SLAs and supports downstream BI, reporting apps, and applied statistics workloads.
• Play a key role in cross‑functional forums (e.g., Data Governance Committee, analytics communities) as the technical voice for feasibility, risk, and long‑term platform health.

Technical Leadership, Mentorship, and Culture

• Lead large, multi‑team technical initiatives—from design to implementation and rollout—setting a high bar for design docs, reviews, and execution quality.
• Mentor senior and mid‑level engineers, elevating the team’s skills in data modeling, pipeline design, governance, and platform thinking.
• Help shape playbooks for how product squads and spokes engage wi