4-6+ years of experience in Data Engineering or Software Engineering with a focus on data.
Proficiency in Python and SQL.
Hands-on experience with cloud data platforms (AWS or GCP) and data warehouses (Snowflake, Redshift, BigQuery).
Familiarity with healthcare data standards (FHIR/HL7, EHR APIs) and managing messy clinical/claims data.
Experience in data modeling for various sources and workflows.
Strong communication skills for explaining technical concepts to diverse stakeholders.
Located in NYC and willing to work in-office 3+ days per week.
Responsibilities
Design, implement, and maintain scalable data pipelines and architectures.
Structure and maintain durable integrations and ETL workflows for client data onboarding.
Build and maintain well-documented data models for reporting and analytics.
Ensure accurate data flow from source systems to products and AI/ML applications.
Implement monitoring and testing to ensure high data quality and system reliability.
Benefits
Generous benefits package including equity options.
Supportive environment for career growth in a rapidly scaling startup.
Opportunity to work on significant healthcare technology challenges.
Access to modern data technologies and practices.
Full Job Description
About the role
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.
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.
We expect you to have:
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.
Target compensation for this role is $170-$250k, plus equity and a generous benefits package.
