Data Engineer - Healthcare

Percepta•New York City, NY

About The Position

Percepta’s mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology. To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together: Forward-deployed expertise in engineering, product, and research Mosaic, our in-house toolkit for rapidly deploying agentic workflows Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives. Percepta is a direct partnership with General Catalyst, a global transformation and investment company. About the role We’re hiring a Data Engineer to partner closely with our product and AI engineering teams to lead AI transformations of large health systems & healthcare providers (e.g., Summa Health). You will help design, build, and operationalize the data systems that power AI applications, while informing decisions on data models, infrastructure, and pipeline architecture. You’ll work hands-on in a complex provider data environment (Epic, claims, scheduling, imaging metadata, SDOH, payer data), helping bring order to messy systems and enabling AI engineers to build high-impact AI workflows. If you enjoy building in ambiguity, forming technical opinions, and shipping value quickly inside complex environments, this role is for you. In this role, you will: Shape how data powers AI applications across large health systems by designing the pipelines and models that drive real clinical and operational improvements. Influence technical strategy for how Percepta deploys AI across dozens of complex health system environments. Build foundational data systems that become reusable patterns for multiple health-system transformations. Work directly with clinicians and operational leaders to turn high-value use cases into production-ready data workflows

Requirements

Hands-on experience building pipelines on Databricks or similar cloud data platforms
SQL and Python proficiency
Experience with streaming tools (Kafka or comparable)
Experience with both relational databases (Postgres, MySQL) and NoSQL/columnar stores (MongoDB, Dynamo, etc.)
Solid understanding of ETL and ELT patterns, orchestration, CI/CD for data, and schema design
Familiarity with FHIR, HL7, Epic data structures, payer and claims datasets
Comfort working with DICOM metadata, scheduling/ADT feeds, SDOH sources, and operational hospital datasets
Understanding of what ML systems need: features, embeddings, context windows, and retrieval patterns
Experience structuring data for RAG, retrieval workflows, or agent-style systems
Experience partnering with ML engineers or supporting ML-adjacent pipelines
Comfortable working inside hybrid cloud environments, or messy enterprise systems
High ownership and ability to operate without perfect requirements
Strong communication skills with comfort being embedded with customer teams

Nice To Haves

Experience working on AWS
Prior startup or forward-deployed data engineering experience
Data engineering inside a hospital or payer

Responsibilities

Build and operate production-grade pipelines
Develop end-to-end pipelines across Epic, claims, financial, scheduling, imaging metadata, and other clinical datasets
Ingest from FHIR APIs, HL7 feeds, SFTP drops, flat files, and streaming sources
Build on Databricks or similar platforms for ingestion, transformation, and feature creation
Work across a mix of Azure and AWS systems, with experience keeping pipelines running smoothly through migration periods
Structure, normalize, and model noisy datasets to support rapid ML/AI development
Navigate fragmented hospital schemas (Epic Clarity/Caboodle, claims tables, ADT feeds, scheduling data) to identify correct sources and relationships.
Shape how data is delivered to models, including features, retrieval schemas, context construction, and embeddings
Build pipelines that support both batch and streaming or near-real-time workflows
Inform decisions on data models, storage, orchestration, and infra tradeoffs
Diagnose data quality issues, missingness, and schema inconsistencies; propose fixes or alternative approaches.
Balance architectural thinking with forward-deployed delivery — moving quickly while making decisions that scale.
Work directly with clinicians, operations leaders, IT, and product teams
Translate business and clinical needs into technical data solutions
Contribute to a repeatable data playbook for Percepta’s AI deployments across health systems