VP, Data Platform

Strive Health

3d•$173,000 - $231,000•Hybrid

About The Position

At Strive Health, patients come first. We’re on a mission to transform chronic conditions by identifying risk earlier, coordinating thoughtful care, and supporting people through every stage of their health journey. Our work reduces emergency visits, improves outcomes, and helps patients live fuller lives. You’ll work alongside passionate Strivers who care deeply about making an impact, show up for one another as One Team, and find ways to elevate the everyday. If you’re looking for meaningful work where your contributions truly matter, you’ll feel right at home at Strive! We are seeking a Vice President of Data Platform to lead Strive’s enterprise data platform, data architecture, and data governance capabilities. This leader will carry end-to-end accountability for the design, management, reliability, and evolution of Strive’s data warehouse and for ensuring that high-quality, well-governed data is available to the teams that depend on it. The role will oversee the ingestion and hydration of the warehouse from a broad range of inbound data streams, including health plan (claims, revenue, benchmarks, adjustment, risk adjustment files), clinical, eligibility, pharmacy, laboratory, Admit, Discharge, Transfer (ADT), partner, and operational data. The Vice President of Data will ensure that data is transformed into trusted, reusable, and well-documented data products for Digital Product, Software Engineering, Data Science, Analytics, Medical Economics, Actuarial, Finance, Clinical, and Operations teams. As a key member of the Technology leadership team, this leader will help establish data as a durable enterprise capability and a strategic foundation for clinical excellence, operational performance, artificial intelligence (AI), and value-based care. We are looking for a candidate with strong leadership judgment, an entrepreneurial spirit, and excitement for building and scaling a trusted, enterprise data capability in a complex, mission-driven health-tech environment.

Requirements

10+ years of professional experience in data engineering, data architecture, analytics engineering, or a related discipline, including experience building and operating enterprise-scale data platforms.
8+ years of experience leading technical data teams, including responsibility for hiring, developing, managing, and aligning data engineers, architects, analytics engineers, or related technical professionals.
7+ years of experience designing and evolving a data warehouse, lakehouse, or comparable enterprise data platform serving multiple technical, analytical, and business stakeholders.
5+ years of experience owning production operations for data pipelines and warehouse workloads, including monitoring, observability, alert triage, escalation, recovery, reprocessing, incident response, and preventive remediation.
5+ years of experience integrating complex and heterogeneous data sources, including structured, semi-structured, transactional, clinical, claims, or partner data.
5+ years of experience building governed data models, datasets, marts, semantic layers, or other reusable data products for downstream technical and business consumers.
3+ years of experience establishing or operating data governance and data-quality practices, including ownership, stewardship, lineage, documentation, data definitions, access controls, and issue management.
5+ years of experience partnering with Digital Product, Software Engineering, Data Science, Analytics, Finance, Actuarial, Medical Economics, Clinical, or Operations teams to deliver trusted data products, reporting, measures, or model inputs.
Internet Connectivity - Min Speeds: 3.8Mbps/3.0Mbps (up/down): Latency <60 ms.
Ability to travel and be on-site to meet business needs.

Nice To Haves

Experience in healthcare, healthtech, health plans, provider organizations, value-based care, or another regulated environment where data quality, privacy, and auditability materially affect business and clinical outcomes.
Experience with healthcare data and interoperability standards, including medical and pharmacy claims, clinical records, lab and ADT data, FHIR,HL7), eligibility, utilization, quality, or risk-adjustment data.
Experience working with modern cloud data platforms, relational and distributed data stores, ELT/ETL) frameworks, orchestration tools, APIs, event streams, and change-data-capture patterns.
Experience improving data freshness through selective use of near-real-time or streaming architectures where the business value justifies the added complexity and cost.
Experience supporting actuarial, medical economics, risk adjustment, quality, financial, or population-health models and reporting.
Experience supporting machine-learning or AI systems through reliable feature pipelines, training datasets, inference inputs, monitoring, and data lineage.
Experience leading data-platform modernization, technical-debt reduction, source-system consolidation, or migration from fragmented legacy data environments.

Responsibilities

Own the vision, strategy, architecture, operating model, and long-term roadmap for Strive’s enterprise data warehouse and related data platform capabilities.
Carry end-to-end accountability for the routine operation, production support, reliability, and maintenance of warehouse ingestion and hydration across claims, clinical, eligibility, pharmacy, laboratory, ADT, partner electronic health record (EHR), financial, and operational data sources.
Establish scalable ingestion, transformation, orchestration, and data-modeling patterns-including batch, incremental, change-data-capture, and streaming approaches-with appropriate attention to latency, cost, resiliency, recoverability, and supportability.
Build and operationalize scalable processes that enable Analytics, Data Science, and Medical Economics teams to develop trusted metrics, reporting, and insights in the data warehouse.
Establish optimization and efficiency practices that keep the warehouse running quickly and reliably for analytical workloads, and implement governance to identify, retire, and document outdated or redundant metrics.
Lead Strive’s standard data governance initiatives and ensure that all teams follow consistent protocols across the warehouse.
Design and operate a production observability and alerting model for ingestion, transformation, warehouse loads, dependencies, and downstream data products, covering freshness, completeness, volume, schema changes, accuracy, reconciliation, latency, availability, pipeline health, and downstream impact to ensure data quality.
Own failed or degraded data-processing incidents from triage through verified closure, including severity assessment, alert routing, escalation, stakeholder communication, recovery, reruns, reprocessing, root-cause analysis, corrective actions, and improvements to mean time to detect and mean time to resolve.
Establish and lead a durable data governance and quality-management process, including ownership, stewardship, data contracts, definitions, lineage, access, retention, prioritization, automated validation, reconciliation, anomaly detection, and issue resolution.
Partner with Digital Product, Software Engineering, Data Science, Analytics, Medical Economics, and Actuarial teams to support reliable downstream consumption and production deployment, monitoring, and lifecycle management of analytical and machine-learning models.
Partner with Security, Privacy, Legal, Compliance, Clinical, and Technology leadership to make governed data available through reliable interfaces, application programming interfaces (APIs), events, and other appropriate consumption patterns while protecting Protected Health Information (PHI).
Lead remediation of recurring data-quality issues and ingestion technical debt, and define operating metrics for freshness, completeness, accuracy, availability, alert quality, incident recurrence, service-level adherence, and request fulfillment.
Recruit, develop, and retain the data organization; establish the right mix of data engineers, data architects, analytics engineers, evaluate and manage vendors, and consultants; and communicate data risks, tradeoffs, priorities, and production health clearly to executive and cross-functional stakeholders.