Sr. Data Engineer

Clipboard•San Francisco, CA

71d•Remote

About The Position

Clipboard Health is seeking a Senior Data Engineer to join their Data Engineering team. This role is crucial for ensuring Clipboard's data and knowledge infrastructure is reliable and well-governed, serving both human analysts and AI agents. The engineer will build systems to make key business data and definitions easily accessible, preventing information from being siloed or rediscovered. A significant part of the role involves building for AI as a first-class consumer, designing systems that close the knowledge loop for AI-assisted analytics. This includes developing agentic workflows, peer-reviewed artifact creation, and structured knowledge trees. The position also requires maintaining the foundational data pipelines (extract, load, transform) into the data warehouse, ensuring availability and freshness. Additionally, the role involves managing the access control framework for compliance and supporting engineering teams in building, deploying, and monitoring ML models. The team prioritizes understanding and solving customer problems systematically, measuring success by the reliability, adoption, quality, and speed of decisions enabled by data.

Requirements

First-principles thinking: ability to dig into source systems, metric discrepancies, or slow pipelines before deciding on a course of action.
Customer-centricity: ability to stay close to stakeholders, understand the decisions their data enables, and prioritize focus on the right problems.
Ownership and judgement: comfort owning infrastructure that others depend on and treating responsibility for pipeline reliability and metric accuracy as one's own business.
Technical strength spanning pipelines, semantic modeling, governance, and AI-facing knowledge infrastructure.
Experience with Snowflake as a data warehouse.
Experience with dbt for transformation.
Experience with Airbyte and Hevo for ingestion.
Experience with Hex and Metabase for BI.
Experience with source systems like MongoDB and Postgres.

Responsibilities

Build systems that make key business data and definitions easily accessible.
Develop workflows for capturing meaning as governed, versioned artifacts (dbt semantic models, Snowflake views, structured knowledge files).
Design and build systems that close the knowledge loop for AI-assisted analytics, including agentic workflows, peer-reviewed artifact creation, and structured knowledge trees.
Maintain the foundational data pipelines that extract, load, and transform data from source systems into the warehouse.
Manage the access control framework (Snowflake roles, PII/PHI provisioning, least-privilege at scale) in collaboration with the Security team.
Support engineering teams' needs for building, deploying, and monitoring ML systems as the company invests in training and hosting production ML models.