Sr. Data Engineer

Clipboard•San Francisco, CA

20h•Remote

About The Position

Clipboard is a remote-first company with over 1,000 employees, founded in 2016. Our mission is to uplift communities by connecting healthcare professionals with workplaces through an app-based marketplace. This enables financial stability for healthcare workers and provides essential care to millions across the U.S. We are the leader in Long-Term Care staffing and are expanding into Home Health, Hospitals, and more. Data Engineering at Clipboard is integral to the business, with engineers owning the full software development lifecycle. The data pipelines, models, and tooling built by Data Engineers are critical for decision-making in operations, finance, product, and AI-assisted workflows. This role focuses on making Clipboard's data and knowledge infrastructure reliable and well-governed for all decision-makers, including human analysts and AI agents. The Senior Data Engineer will build systems to ensure easy access to critical business data and definitions, such as net revenue calculations or the meaning of a "verified shift." The goal is to move this knowledge from individual understanding to governed, versioned artifacts like dbt semantic models, Snowflake views, and structured knowledge files, making them reusable across the organization. A significant aspect of this role involves building for AI as a primary consumer, ensuring AI-assisted analytics is supported by high-quality data and knowledge context. This includes developing agentic workflows, peer-reviewed artifact creation, and structured knowledge trees to enhance future analysis sessions. The role also involves maintaining the core data infrastructure, including pipelines for data extraction, loading, and transformation into the data warehouse, ensuring availability and freshness. Additionally, the engineer will manage the access control framework (Snowflake roles, PII/PHI provisioning) in collaboration with the Security team for compliance. As the company invests in ML models, support will be provided for building, deploying, and monitoring these systems. The team prioritizes understanding and solving stakeholder problems with systematic solutions, measuring success by the reliability, adoption, quality, and speed of decisions enabled by data.

Requirements

First-principles thinking: ability to investigate source systems, metric discrepancies, or slow pipelines before deciding on a course of action.
Customer-centricity: ability to stay close to stakeholders, understand their data needs, 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.
Interest in the full scope of data engineering rather than staying in one specialized lane.

Responsibilities

Build systems that make critical 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 for AI-assisted analytics, including agentic workflows, peer-reviewed artifact creation, and structured knowledge trees.
Maintain 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 in building, deploying, and monitoring production ML models.