Responsibilities:

1. Data Ingress & Decoupled Architecture

• Lead the product vision and roadmap to decouple core data ingestion modules from operational API boundaries, turning them into highly scalable, standalone micro-services.
• Productize event-driven and streaming ingestion pipelines (e.g., Kafka/Spark Structured Streaming) capable of processing billions of high-velocity records.
• Establish clear API contracts, event interfaces, and integration specs for external sources feeding raw payloads (HL7 v2, C-CDA, FHIR JSON, Claims) into our platform.

2. Big Data & Medallion Analytics Strategy

• Define requirements for transitioning mass analytics pipelines onto a Databricks / Medallion Architecture using Apache Spark.
• Drive the design of optimized Silver and Gold layer datasets, ensuring deeply nested JSON formats (e.g., FHIR) are efficiently flattened and structured into high-performance columnar models (Delta Lake/Parquet) for OLAP analytics.
• Partner with engineering architects to balance real-time, low-latency micro-batches against cost-optimized, high-throughput daily batch workloads.

3. Master Data Management (MDM) & Identity Resolution

• Own the product requirements for our FHIR-native MDM Engine, defining rules for deterministic and probabilistic patient matching at scale.
• Productize Survivorship Logic to establish rules for creating and maintaining the "Golden Record" across fragmented data feeds.
• Design crosswalk management capabilities to track source-to-target entity linkages across millions of patient lives.

4. Error Remediation & Exception Management

• Build out the end-to-end exception lifecycle for data validation failures—from isolation in Dead-Letter Queues (DLQs) to structured error reporting.
• Define requirements for Data Steward interfaces and APIs, allowing users to review validation exceptions, perform manual record merges/unmerges, and correct bad data payloads.
• Architect Idempotent Replay Mechanisms to re-ingest corrected payloads through Spark processing pipelines without generating duplicate data or corrupting state.

Requirements:

Must have Experience 
5+ years of Technical Product Management experience leading complex backend data platforms, distributed data engineering products, or big-data ingestion systems.
Hands-on Distributed Computing Knowledge: Proven track record productizing solutions powered by Apache Spark (batch or streaming) and distributed data processing frameworks.
Databricks / Lakehouse Expertise: Deep familiarity with Medallion Architecture (Bronze/Silver/Gold) and Delta Lake/Parquet performance patterns.
OLAP / Columnar Strategy Mindset: Demonstrated experience optimizing complex, highly nested arrays and hierarchical data structures for analytical query engines.
Identity & Governance: Direct experience productizing Master Data Management (MDM), entity resolution, or identity matching tools.
Fault-Tolerant Systems: Experience with event-driven architectures, Dead-Letter Queues (DLQs), and data replay/remediation workflows.
Technical Proficiency: Ability to comfortably write and execute SQL and Python to analyze raw datasets, validate engine output, and define logic rules
Nice-to-Have Experience
Experience with healthcare data standards, including HL7 FHIR, C-CDA, or HL7 v2.
Exposure to healthcare terminologies (SNOMED CT, LOINC, RxNorm, ICD-10).
Familiarity with SQL-on-FHIR analytics standards.