Data Engineer

Sage Care; Palo Alto (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Sage Care is seeking a data engineer to own the core data management system, integrating with healthcare systems like EPIC and building scalable storage that remains HIPAA compliant. You will develop a UI to view and manage patient and provider data, and will extract knowledge from real data to support provider matching and care navigation.

The role requires 5+ years in data engineering, with a focus on data extraction, summarization, and collaboration with end users to iterate on solutions.

Qualifications

• 5+ years of data engineering experience.
• Experience with data extraction and summarization.
• Experience working closely with end users in iterative fashion gathering feedback.

Responsibilities

• Build and own the core data management system.
• Build data fetch solutions via integration with systems like EPIC.
• Build flexible, scalable, HIPAA-compliant storage solutions.
• Build user facing UI to view and manage data.
• Extract knowledge from real data and summarize results.
• Extract high level properties from raw data.

Skills

Data engineering

Data extraction

Summarization

Backend engineering

Systems thinking

User feedback

Tools

EPIC

Job description

About Sage Care

Sage Care is a fast-growing, early-stage healthcare startup founded by exceptional leaders from Apple, Uber, Carbon Health and backed by top-tier venture capital (General Catalyst, Chelsea Clinton). With a strong customer pipeline, Sage Care is transforming healthcare by simplifying care navigation.

Our platform makes it easier for patients to find the right doctor and helps providers focus on those who need them most through harnessing the latest AI innovations.

Building on our successful collaborations with health systems across the U.S., we have expanded internationally to the MENA region. We are now partnering with health systems there to deploy our AI-powered care navigation platform.

About The Role

Our products rely on management and semantic projection of data like Provider (doctor) information, Referrals, Patient charts and history, workflow SOPs, etc. The goal is to fetch this data from our partners, store it, extract high level semantic projections (knowledge) from the data and help power our key products including Provider Matching, Scheduling, Referral Recommendation engine and AI voice agents. In this role, you will own the full lifecycle of the data including User facing UI to view the data along with ability to update it when needed.

This role sits at the intersection of Data management, ML/AI research and software engineering. We’re looking for someone who can help build out a scalable data management and extraction solution. You will work closely with engineers who work on the matching software, medical experts and professionals who can help guide the knowledge extraction.

What You’ll Do

Build and own the core data management system.

• Build Data fetch solutions via integration with systems like EPIC, etc.
• Build flexible, scalable and efficient storage solutions that are HIPAA compliant
• Build user facing UI to view and manage data

Extract knowledge from real data

• Build knowledge extraction layer on top of the raw data
• Summarize key results from Provider notes
• Extract high level important properties from raw data

What We’re Looking For

• 5+ years of Data engineering experience.
• Experience working with data extraction and summarization.
• Experience working closely with end users in iterative fashion gathering feedback along the way.
• Strong backend engineering skills and systems thinking.
• Experience working with ambiguous problems and defining solutions from first principles.
• Experience turning novel techniques into testable prototypes.

Nice to Have

• Experience with evaluation frameworks and quality measurement.
• Experience with medical AI systems.
• Experience with extraction of text/data from PDFs and Images.

Example Things you’ve Done

• Built extensible data storage systems for medical data across multiple partners.
• Built data extraction, summarization, property tagging systems on top of structured or unstructured text.
• Built UI to display, update and search on top of underlying storage.