Data Engineer

About the Role

As a Data Engineer at Pivotal Health, you will be part of a dynamic team that is passionate about simplifying complex reimbursement workflows for healthcare providers. Your work will directly impact the company's ability to deliver high-quality services, reduce administrative burdens, and recover rightful reimbursements for providers.

Pivotal Health combines software, data, and service into a seamlessly integrated, AI-driven platform. The company's full-service IDR solution is designed to efficiently dispute underpaid claims, and the team is building solutions that enable providers to operate with clarity, control, and confidence across the reimbursement journey.

What You Will Do

• Own the pipeline from the product database to the analytics warehouse, taking full ownership of extracting data from the PostgreSQL product database and loading it into BigQuery.
• Design and maintain ETL processes that make data extraction reliable, with the right structure for downstream analytics use.
• Bring in new data sources, including third-party tools like Salesforce, into the warehouse, partnering with the DevOps team to establish the right service accounts, permissions, and connection patterns.
• Build and maintain analytics-ready tables using dbt, designing, building, and managing the transformation layer that turns raw data into clean, well-structured tables.
• Support reporting and business insights, working alongside analysts to ensure data is fresh, accurate, and structured in a way that makes building dashboards and reports reliable and efficient.
• Serve as the bridge between analytics and engineering, attending engineering team meetings to stay ahead of product changes that could affect analytics.
• Translate data needs into technical solutions, keeping both teams aligned and ensuring that data engineering efforts support business objectives.
• Collaborate with the DevOps team to ensure secure and correct integration of new data sources.
• Participate in the design and implementation of data quality checks to ensure the integrity of the data.

What We Are Looking For

• Strong SQL skills with hands-on experience in modern cloud data warehouses: BigQuery, Snowflake, or Redshift.
• Proficiency with dbt for managing SQL transformations, understanding how to write clean, maintainable, well-documented models.
• Comfortable with Python at a working level, enough to build and automate data workflows without needing to be a full software engineer.
• Experience with at least one BI or reporting tool: Tableau, Power BI, Metabase, or similar.
• A mindset that thinks in business outcomes, with a resume that reflects the impact of your work, not just the tools you used.
• Self-directed and comfortable with ambiguity, able to identify what needs to be done and execute without heavy guidance.
• Collaborative by nature, with experience working with cross-functional teams.
• Strong understanding of data modeling and data warehousing principles.
• Experience with data quality and integrity processes.

Nice to Have

• Experience with Salesforce integration, having worked with Salesforce data in a previous role.
• Knowledge of machine learning principles and their application in data analysis.
• Certification in data engineering or a related field, demonstrating a commitment to professional development.
• Experience with agile development methodologies, having worked in fast-paced environments.

Benefits and Perks

• Competitive salary range of
  60,000 -

  80,000, reflecting the value Pivotal Health places on its technical talent.
• Opportunities for professional growth and development, with a focus on helping you achieve your career goals.
• Comprehensive health insurance, recognizing the importance of your well-being.
• Generous PTO policy, allowing you to recharge and pursue your interests.
• Remote work stipend, supporting your ability to work from anywhere.
• Equity in a company that is changing the healthcare landscape, offering a unique opportunity for long-term financial growth.
• Access to cutting-edge technologies and tools, ensuring you stay at the forefront of your field.

• Be prepared to demonstrate your proficiency in SQL and experience with cloud data warehouses like BigQuery, highlighting specific projects or accomplishments.
• Show a deep understanding of dbt and its application in data transformations, discussing how you've used it to improve data quality and accessibility.
• Emphasize your ability to work independently and collaboratively, giving examples of how you've managed ambiguity and driven projects forward with minimal guidance.
• Prepare to discuss how you think about business outcomes and how your work as a Data Engineer can drive meaningful insights and decisions, using real-world examples from your experience.
• When discussing your experience with BI or reporting tools, focus on the insights you've gained and how you've used those tools to tell a story with data, rather than just listing technical skills.
• Be ready to walk through your approach to data quality and integrity, including any processes or checks you've implemented in previous roles to ensure data accuracy and reliability.