Senior Manager, Platform Product (Remote)

The Healthcare Analytics Solutions (HAS) Data Platform Product Manager is a highly technical role responsible for collaborating across the organization to define the enterprise-grade HAS data platform roadmap architecture and federated governance framework to support business and revenue objectives.

The data platform incorporates high-volume diagnostic data generated by Quest operations as well as clinical data from external sources. The Platform Manager will drive technical strategy architectural decisions and API-first design patterns to ensure that data is ingested standardized and modeled in a way that enables real-time stream processing complex semantic modeling and advanced GenAI-driven analytics for internal and customer-facing solutions.

This role will work closely with Data Engineering Solution Delivery Innovation & Architecture and Quest Technology (IT) to build robust secure cost-optimized and compliant data pipelines semantic layers and generative AI features.

Core Platform & Technical Strategy:
Partner with HAS business units Data Engineering and Quest IT to define architect and manage the data platform strategy data contracts and technical roadmap.
Drive detailed technical requirements for data architecture dimensional/relational data modeling and automated integration pipelines (ETL/ELT and streaming) to support the HAS product portfolio.
Establish and monitor platform performance metrics including SLAs SLOs and SLIs for data freshness pipeline latency and platform uptime. Serve as the primary technical liaison between business needs and IT engineering translating complex business objectives into rigorous technical specifications API definitions and database schemas.

Data Engineering Streaming & Observability:
Define and enforce Data Contracts guarantee schema stability and prevent upstream changes from disrupting downstream products.
Drive specifications for automated low-latency ingestion pipelines evaluating performance and partition strategies across both batch processes and real-time streaming architectures (e.g. Apache Kafka AWS Kinesis).
Design and implement automated Data Observability frameworks (e.g. using Great Expectations Monte Carlo or Soda) to monitor data quality schema drift and lineage in production.
Perform hands-on data profiling complex SQL querying query optimization and exploratory data analysis (EDA) to validate platform datasets and troubleshoot integration issues.

Advanced Analytics & GenAI Readiness:
Be familiar with and able to review technical requirements schema designs and data models for a semantic information layer optimized for agentic workflows vector databases and knowledge graphs (RAG pipelines).
Collaborate with Advanced Analytics and Architecture teams to design the HAS semantic data layer to reduce dependency risks for complex product integrations.
Partner with Business Product teams and Data Scientists to incorporate complex semi-structured and unstructured healthcare data types (e.g. EMR notes pathology reports molecular diagnostics).

Governance & Operations:
Partner with engineering and data teamsto define standardize and promote automated CI/CD and DataOps/MLOps processes that accelerate and secure the data product lifecycle.

Ensure platform compliance with HIPAA PHI handling and data privacy security controls through robust column-level encryption masking role-based access control (RBAC) and automated access governance.

Required Work Experience:

5 years of technical product management experience in data platforms data engineering or advanced analytics technology.
3 years of hands-on experience as a Data Analyst Data Engineer or in a highly technical role directly writing queries modeling data and testing pipelines.
Proven experience managing platform products through the software development lifecycle including writing technical specifications system architecture design API definitions and agile backlog management.

Preferred Work Experience:

Deep experience with healthcare data standards and interoperability protocols (HL7 v2/v3 FHIR DICOM).
Experience building or managing decentralized data architectures (Data Mesh) and defining data products.
Strong working knowledge of modern data stack orchestration and transformation tools (e.g. dbt Apache Airflow Prefect).
Experience with Infrastructure as Code (IaC) principles (e.g. Terraform) and cloud financial management (FinOps) for data warehousing cost optimization.
Demonstrated experience presenting complex engineering blueprints and system designs to senior leadership and non-technical stakeholders.

Technical/Job Specific Knowledge:

Cloud & Data Warehousing: Deep expertise with cloud-based data platforms (AWS GCP) and modern enterprise data warehousing/lakehouses (Snowflake BigQuery Databricks) including knowledge of cluster tuning and cost controls.
Streaming & Message Queuing: Familiarity with event-driven architectures streaming platforms (Apache Kafka Flink) and schema registries.
Data Engineering & Ops: Strong understanding of distributed computing CI/CD pipelines automated testing of data pipelines and DataOps/MLOps concepts.
AI & Graph Tech: Conceptual and practical understanding of Vector Databases (Pinecone pgvector) Graph Databases (Neo4j) Knowledge Graphs and LLM orchestration frameworks (e.g. LangChain LlamaIndex) for semantic search and RAG.
Product Tooling: Deep familiarity with technical product management tools (Jira Confluence Git GitHub).

Skills:

Advanced SQL Proficiency: Mastery of complex SQL queries (window functions CTEs query optimization) for independent data profiling troubleshooting and ad-hoc analysis.
Programming/Scripting: Proficiency in Python or Scala for basic scripting data manipulation (Pandas PySpark) and REST API interaction.
Schema Definition: Ability to write read and version schema definition files (Avro JSON Schema Protobuf).
Technical Translation: Proven ability to translate complex engineering constraints database schemas and architectural bottlenecks to business teams and vice-versa.

Required Education:

Bachelors degree in Computer Science Data Engineering Information Systems Mathematics or related highly technical field.

Preferred Education:

Data Engineering or a related field.