Job description

Job Description 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. Responsibilities: 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 teams to 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. Qualifications: 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: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Mathematics, or related highly technical field. Preferred Education: Data Engineering, or a related field. 69471 Quest Diagnostics honors our service members and encourages veterans to apply. While we appreciate and value our staffing partners, we do not accept unsolicited resumes from agencies. Quest will not be responsible for paying agency fees for any individual as to whom an agency has sent an unsolicited resume. Equal Opportunity Employer: Race/Color/Sex/Sexual Orientation/Gender Identity/Religion/National Origin/Disability/Vets or any other legally protected status.

Requirements

Must-haves

Responsibilities: 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.
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.
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).
Partner with engineering and data teams to 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.
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.
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.
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.
Product Tooling: Deep familiarity with technical product management tools (Jira, Confluence, Git, GitHub).
Advanced SQL Proficiency: Mastery of complex SQL queries (window functions, CTEs, query optimization) for independent data profiling, troubleshooting, and ad-hoc analysis.

Nice-to-haves

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