Data Engineer

Thesis•New York, NY

29d•$170,000 - $250,000•Hybrid

About The Position

Thesis is building the AI-powered care team platform for infinitely scalable clinical capacity. We radically increase access and improve quality of care by combining AI agents with clinical experts to take on high-impact clinical operations and care management activities for healthcare organizations. We’re based in NYC, are growing rapidly, and are backed by $60 million in funding from Oak HC/FT, CRV, Black Opal Ventures, and experienced C-level healthtech angel investors. We are looking for a highly driven Data Engineer to help design, build, and scale the data foundations that power Thesis’s AI agents, including owning critical client integrations. You will work closely with the product, engineering, and AI/ML teams to ensure Thesis’s data architecture is scalable, reliable, secure, and actionable. This is a broad-scope, high-impact role for a technical builder who thrives in ambiguity and wants meaningful ownership over modern data infrastructure in an early-stage healthcare startup.

Requirements

Strong technical foundation: 4-6+ years of experience in Data Engineering or Software Engineering with a data focus; strong proficiency in Python and SQL.
Modern data stack experience: Hands-on experience with cloud data platforms (AWS and/or GCP), data warehouses (Snowflake, Redshift, or BigQuery), orchestration tools (Airflow or similar), and transformation tools (dbt preferred).
Healthcare data chops: Familiarity integrating with healthcare data standards and systems (e.g., FHIR/HL7, EHR APIs), including working with messy clinical/claims data.
Data modeling expertise: Experience designing data models for different sources, workflows, and business processes.
Clear communication: Ability to explain complex technical concepts to both technical and non-technical stakeholders.
NYC-based: You are based in New York and excited to be in-office 3+ days per week.

Responsibilities

Build core data infrastructure: Design, implement, and maintain scalable, data pipelines and architectures across ingestion, transformation, and storage layers
Own client integrations and data ingestion: Structure and maintain durable integrations and ETL workflows to reliably onboard and normalize client data.
Design robust data models: Build and maintain clean, well-documented data models that support reporting, analytics, and downstream automation.
Enable product and analytics outcomes: Ensure data flows accurately from source systems to products, analytics, and AI/ML use cases.
Drive data quality and reliability: Implement monitoring, testing, and observability to ensure high data quality and system uptime.