Senior Data Engineer

Teal Health is hiring a Senior Data Engineer to take ownership of the company’s data warehouse, ingestion pipelines, and modeled Omni data layer. In this hybrid United States contract role, you will build analysis-ready models so teams can confidently query consistent data across forecasting and key operational domains.

You will work end to end, starting from raw sources and moving through semantic modeling and dbt transformations, while partnering with analytics to ensure dashboards reflect an accurate, well-defined data layer. The work includes ongoing integrations, documentation, and careful handling of sensitive health information in line with HIPAA and Teal’s data governance practices.

What you’ll own

• Own Teal’s data warehouse, including architecture, performance, reliability, and documentation.
• Manage and extend Fivetran ingestion across source systems, troubleshooting connector and schema issues as they arise.
• Build and maintain the dbt transformation layer, following a staging reporting pattern from raw sources to business-ready models used for forecasting, marketing, HubSpot, AWS, PostHog, and inventory.
• Own the Omni data layer, defining relationships, joins, and modeled fields that enable cross-system answers on demand.
• Partner with the analytics lead so dashboards rely on a data layer that is accurate, consistent, and well-defined.
• Take on integrations early, including connecting new data sources and supporting partner data flows.
• Write dbt tests and documentation so the team can trust and understand what’s in the warehouse.
• Set standards for data modeling, testing, and documentation, helping shape how the data team grows.
• Work from a ticketed backlog (Jira or similar), scoping requests across the company and keeping priorities visible.
• Handle sensitive health data with care in line with HIPAA and Teal’s data governance practices.

Core qualifications

• 5+ years of experience in data engineering or analytics engineering, including ownership of a production data warehouse.
• Strong SQL, including window functions, date logic, and complex joins across marketing, product, and operational data.
• Fluency in Postgres/Redshift/Snowflake-style SQL features (for example: ::type casts, date_trunc()).
• Hands-on experience with dbt-core, including:
  • Models and sources
  • dbt_project.yml and packages.yml configuration
  • Jinja templating
  • Custom macros (e.g., generate_schema_name)
  • dbt testing and docs framework
• Experience designing and developing a data warehouse using a layered staging reporting architecture.
• Experience with Fivetran-landed data and comfort investigating source-specific raw schema quirks.
• Working knowledge of common dbt packages such as dbt_utils and dbt_date (including surrogate keys and date spines).
• A habit of clear documentation and organized, ticket-driven work.

Technologies involved

• SQL, Postgres, Redshift, Snowflake
• dbt-core, Jinja
• Fivetran
• dbt_utils, dbt_date
• Omni
• HubSpot, PostHog, AWS
• HIPAA, Jira
• Ad platforms: Google, Meta, TikTok, Bing, Reddit, Pinterest, Quora, Spotify Ads

Role details

• Location: United States (hybrid). Preferred hybrid or full-time in office (San Francisco); open to Remote-US.
• Employment type: Contract (~5 to 20 hours per week as projects fluctuate).
• Department: Engineering
• Reports to: Head of Engineering
• Hourly rate: $90–$125 per hour

Preferred experience

• Experience with Omni or a similar semantic/modeling layer (e.g., Looker, LookML).
• Python for scripting, integrations, or pipeline tooling.
• Experience with healthcare or EHR integrations.
• Deeper Jinja experience, including control flow and variables within dbt models.
• Experience at an early-stage or fast-growing company where you owned a data function with limited support.