Technical Product Manager – AI Platform & Interoperability

We are building a scalable, AI-first healthcare data platform that transforms raw clinical data into actionable intelligence.

As a Technical Product Manager, you will drive the development of core platform capabilities spanning:

• Healthcare data ingestion and interoperability
• Data normalization and canonical modeling
• Temporal data processing and workflow orchestration
• AI-ready data pipelines powering recommendations and patient insights

This is a highly technical, execution-focused role requiring deep collaboration with architects and engineering teams to design and deliver high-scale, reliable platform systems.

1. Healthcare Data Ingestion & Interoperability

• Define and deliver scalable ingestion pipelines for HL7 v2, FHIR R4, and other clinical data formats
• Drive integration with interoperability platforms (e.g., Redox, Mirth)
• Ensure high data quality, schema validation, and fault-tolerant ingestion

1. Data Normalization & Canonical Data Model

• Own the design and evolution of a canonical clinical data model across domains (encounters, medications, labs, notes, etc.)
• Define mapping frameworks for terminologies and coding systems (ICD, SNOMED, LOINC, RxNorm)
• Ensure consistent representation of structured and semi-structured clinical data

1. Data Processing & Workflow Orchestration

• Define systems for event-driven and batch data processing pipelines
• Drive temporal alignment of clinical events to support longitudinal patient views
• Partner with engineering to implement scalable workflows using orchestration frameworks (e.g., Temporal)

1. Platform APIs & Data Services

• Define APIs for:
• Data ingestion and transformation
• Access to normalized patient data
• Downstream consumption for analytics and AI use cases
• Ensure high performance, scalability, and reliability of platform services

1. AI-Ready Data Foundation

• Enable data pipelines that support:
• Clinical decision support
• Patient summaries
• Recommendation systems
• Collaborate with AI/ML teams to ensure data is structured, contextualized, and usable for model development

1. Execution, Roadmap & Delivery

• Translate platform vision into clear product backlog (epics, user stories, acceptance criteria)
• Prioritize and sequence work across ingestion, processing, and platform services
• Drive execution with engineering teams from 0 → 1 → scale
• Track success via platform reliability, performance, and downstream impact

• Designing a unified clinical data model across fragmented healthcare data sources
• Building high-throughput ingestion systems for real-time and batch clinical data
• Creating temporal patient timelines from multi-source healthcare events
• Enabling AI-driven insights through clean, normalized, and structured datasets
• Solving data consistency, mapping, and interoperability challenges at scale

• 5–8+ years of product management experience in a technical domain
• Strong experience building platforms, APIs, or data infrastructure products
• Ability to work deeply with engineers on architecture, system design, and trade-offs
• Proven track record of delivering scalable systems from concept to production

• Experience with HL7 v2, FHIR R4, and healthcare data exchange standards
• Familiarity with interoperability engines (e.g., Redox, Mirth, Rhapsody)
• Understanding of clinical data domains and workflows

• Workflow orchestration frameworks (e.g., Temporal)
• Graph databases (e.g., Neo4j, Amazon Neptune)
• Cloud platforms (Azure preferred, AWS acceptable)
• Distributed systems and event-driven architectures

• Exposure to AI/ML or LLM-based systems
• Understanding of:
• Structured + unstructured data pipelines
• Retrieval-augmented generation (RAG)
• Clinical summarization or recommendation systems

• Strong understanding of:
• Data modeling and schema design
• ETL / ELT pipelines
• Batch and streaming architectures
• Experience working with PostgreSQL or similar relational databases

• AI-first, AI-fast approach to product development
• Strong ownership and bias for execution
• Comfortable operating in ambiguity and solving complex problems
• Deep curiosity and willingness to engage at technical depth
• Outcome-driven mindset — focused on impact, not output

• A scalable platform that reliably ingests and processes healthcare data at scale
• Well-defined canonical data model enabling consistent downstream usage
• Efficient data pipelines powering AI-driven insights and applications
• Strong collaboration between product, engineering, and data teams
• Faster delivery of new platform capabilities and AI use cases

• You prefer high-level strategy without technical depth
• You are not comfortable working closely with engineering teams
• You need rigid processes and detailed instructions
• You are not excited about building AI-driven platforms