Data Engineer

Role overview

Pivotal Health is hiring a Data Engineer at the intersection of analytics and engineering. You’ll build and maintain data infrastructure that makes product data accessible, reliable, and analysis-ready, enabling faster reporting and more trustworthy business insights.

Responsibilities

• Pipeline ownership: Extract data from the PostgreSQL product database and load it into BigQuery.
• ETL design & reliability: Build and maintain dependable ETL processes with the structure needed for downstream analytics.
• Integrate new data sources: Expand the analytics footprint by integrating additional sources (e.g., Salesforce) into the warehouse.
• Secure connectivity: Partner with DevOps to establish service accounts, permissions, and secure connection patterns.
• Analytics-ready modeling: Use dbt to design, build, and maintain the transformation layer into clean, well-structured tables.
• Support analytics & reporting: Ensure data is fresh, accurate, and structured for tools like Tableau, Power BI, and Metabase.
• Bridge analytics and engineering: Stay aligned with product/engineering changes that may affect analytics needs.

Requirements

• Strong SQL skills and hands-on experience with modern cloud data warehouses: BigQuery, Snowflake, or Redshift.
• Proficiency with dbt for SQL transformations (including clean, maintainable, well-documented models).
• Comfort with Python at a practical level to automate and build data workflows.
• Experience with at least one BI/reporting tool (Tableau, Power BI, Metabase, or similar).
• Ability to think in business outcomes and demonstrate impact.
• Self-directed, comfortable working with ambiguity; collaborative communication style.

Nice-to-haves

• Experience integrating third-party SaaS tools into a warehouse (e.g., Salesforce).