Who we are

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

What you’ll do

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

Enable forward-deployed AI builds

• 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

Be a technical partner in architecture and design

• 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.

Collaborate with cross-functional stakeholders

• 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

What we’re looking for

Strong technical foundations

• 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

Healthcare data experience

• 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

AI and ML intuition

• 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

Thrives in ambiguity

• 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

Bonus if you have

• Experience working on AWS

• Prior startup or forward-deployed data engineering experience

• Data engineering inside a hospital or payer