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

The Role 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. What You'll Own Healthcare Data Ingestion & Interoperability - 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 Revenue Cycle Data & Intelligence - 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 Data Normalization & Canonical Data Model - 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) Data Processing & Workflow Orchestration - 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) Platform APIs & Data Services - 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 AI-Ready Data Foundation - 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 Execution, Roadmap & Delivery - 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 Examples of Problem Spaces You'll Tackle - 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 Required Experience Technical Product Management (Must-Have) - 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 Revenue Cycle Management (Must-Have) - 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) Healthcare & Interoperability (Strongly Preferred) - 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 Data & Platform Expertise - Strong understanding of: - Data modeling and schema design - ETL / ELT pipelines - Batch and streaming architectures - Experience working with PostgreSQL or similar relational databases Preferred Technical Stack Experience - 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 AI / ML (Preferred) - 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) Builder Mindset (Non-Negotiable) - 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 What Success Looks Like - 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 This Role Is Not For You If - 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 Why Join Us You'll be building the core infrastructure that powers AI in healthcare, enabling: - Scalable clinical and financial data platforms - Real-time clinical and revenue cycle intelligence - Next-generation AI applications This is a high-impact role at the intersection of healthcare, data, revenue cycle, and AI.