Senior Product Manager, Analytics & Data Platforms
United StatesFull-TimeSenior
Salary132900 - 158400 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- SparkData modelingDatabricksAWS LambdaDistributed Systems
Requirements
- 7+ years of product management experience in agile, cross-functional environments building digital or data-intensive products
- Strong experience owning products involving data pipelines, analytics systems, or reporting platforms
- Deep understanding of data modeling, ETL processes, data validation, and governance practices
- Proven ability to operate in ambiguous, high-pressure environments and drive outcomes independently
- Experience collaborating closely with data engineers and backend teams on system design and implementation
- Strong technical fluency with distributed systems and data platforms, including performance and architecture tradeoffs
- Exceptional communication skills with the ability to translate complex technical concepts for diverse stakeholders
- Strong consulting mindset with experience building trust in client-facing or contracting environments
- Expertise in requirements definition, acceptance criteria, and definition of done
- Strong analytical and problem-solving skills with a systems-thinking approach
- Experience managing complex environments with multiple dependencies and integrations
- Ability to influence stakeholders, manage risk, and drive alignment in large organizations
- Preferred: experience with Databricks, Spark, AWS data services (S3, Glue, Lambda), healthcare data, CMS programs, FHIR, eCQM, or regulated environments
Responsibilities
- Own product strategy and roadmap across analytics platforms, data pipelines, and reporting systems, ensuring scalability, reliability, and operational efficiency
- Define and prioritize product requirements that modernize legacy workflows, reduce manual processes, and improve system performance
- Translate complex data workflows into clear, actionable product specifications in partnership with engineering and analytics teams
- Collaborate on data architecture decisions including ETL pipelines, data modeling, and distributed processing systems
- Drive improvements in data quality, lineage, traceability, documentation, and validation frameworks
- Lead backlog prioritization and delivery planning across competing reporting and data initiatives
- Coordinate cross-functional execution across engineering, analytics, and stakeholder teams to ensure timely, high-quality delivery
- Identify system inefficiencies and drive structural improvements in team workflows, ownership, and operating models
- Manage stakeholder alignment across technical and non-technical partners, ensuring clarity on data impact and program outcomes
- Proactively identify risks in delivery, data accuracy, and dependencies, implementing mitigation strategies
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