Technical Product Manager – AI Platform, Interoperability & & Revenue Cycle Data

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

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

• Healthcare data ingestion and interoperability across clinical and revenue cycle sources
• Data normalization and canonical modeling that connects clinical, claims, and financial data
• Temporal data processing and workflow orchestration
• AI-ready data pipelines powering recommendations, patient insights, and revenue cycle intelligence

This is a highly technical, execution-focused role. You will work closely with architects and engineering teams to design and deliver high-scale, reliable platform systems.

• Define and deliver scalable ingestion pipelines for clinical and administrative data: HL7 v2, X12 EDI transactions (837, 835, 270/271, 276/277, 278), claims and remittance feeds, flat files, and FHIR R4 APIs where applicable
• Drive integration with interoperability platforms (e.g., Redox, Mirth) and with EHR, practice management, and clearinghouse systems
• Ensure high data quality, schema validation, and fault-tolerant ingestion

• Own the platform capabilities that support RCM workflows across patient access, mid-cycle, and back end: eligibility and benefits, prior authorization, charge capture, coding, claims submission, denials, and payment posting
• Link clinical documentation and encounters to charges, claims, and remittances to create a complete view from encounter to payment
• Enable data foundations for AI use cases such as denial prediction and prevention, coding and documentation gap detection, under-payment identification, and prior authorization automation
• Partner with RCM operations, coding, and finance stakeholders to turn revenue cycle pain points into platform requirements

• Own the design and evolution of a canonical data model across clinical domains (encounters, medications, labs, notes) and financial domains (charges, claims, remittances, payers, contracts)
• Define mapping frameworks for clinical and billing terminologies and coding systems (ICD-10-CM/PCS, CPT, HCPCS, DRG, CARC/RARC, SNOMED, LOINC, RxNorm)
• Ensure consistent representation of structured and semi-structured data, aligned with industry standards (including FHIR resources where appropriate)

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

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

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

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

• Designing a unified data model that connects fragmented clinical and claims sources
• Building high-throughput ingestion systems for real-time and batch clinical, EDI, and remittance data
• Creating temporal patient timelines and encounter-to-payment views from multi-source events
• Enabling AI-driven insights that reduce denials, improve coding accuracy, and speed up reimbursement
• 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

• Hands-on experience building products or data platforms for healthcare RCM (provider, payer, or RCM vendor side)
• Solid understanding of the end-to-end revenue cycle: registration and eligibility, prior authorization, charge capture, coding, claims, denials management, and payment posting
• Working knowledge of X12 EDI transactions (837, 835, 270/271, 276/277) and clearinghouse workflows
• Familiarity with billing code sets and payer concepts (CPT, HCPCS, ICD-10, DRG, CARC/RARC, payer contracts, and fee schedules)

• Experience with HL7 v2 messaging and healthcare data exchange
• Working knowledge of FHIR R4 resources and APIs, including financial resources such as Claim, Explanation of Benefit, and Coverage (deep FHIR implementation expertise is a plus, not a requirement)
• Familiarity with interoperability engines (e.g., Redox, Mirth, Rhapsody)
• Understanding of clinical data domains and how clinical documentation drives billing and reimbursement

• 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, recommendation systems, or RCM automation (e.g., denial prediction, autonomous coding)

• 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 clinical and revenue cycle data at scale
• A well-defined canonical data model that links clinical and financial data for consistent downstream use
• Efficient data pipelines powering AI-driven clinical insights and measurable RCM outcomes (lower denial rates, faster reimbursement, better coding accuracy)
• Strong collaboration between product, engineering, data, and RCM operations 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 have not worked with revenue cycle data or workflows
• You need rigid processes and detailed instructions
• You are not excited about building AI-driven platforms