Director / Senior Director, Data & Analytics

NourishedRx•Stamford, CT

About The Position

NourishedRx is seeking a Director / Senior Director, Data & Analytics to lead the Data, Analytics & Insights function end-to-end. Reporting to the Chief Technology Officer, this leader will own the strategy and operating model for how data is ingested, governed, modeled, analyzed, exchanged, and translated into decisions across the company. The role spans data engineering, business intelligence, reporting, insights, client data exchange, and governance, with responsibility for delivering trusted data capabilities that support our members, healthcare partners, product development, and internal operations. This is explicitly a player-coach role. The right leader will set direction, develop the team, and manage cross-functional priorities while remaining close enough to the work to write and review SQL and dbt models, troubleshoot pipelines and data-quality issues, prototype solutions, and personally unblock high-leverage technical problems. This leader will also partner closely with Engineering leadership to advance AI-assisted and agentic engineering practices across Data and Software Engineering, balancing speed and leverage with strong testing, security, reliability, and governance.

Requirements

5+ years of progressive experience in data, analytics, business intelligence, data engineering, or related functions, including meaningful leadership experience managing data teams.
Experience leading a combined data engineering and analytics/insights function, or closely partnering across both disciplines in a high-growth environment.
Strong hands-on SQL fluency and comfort working directly with dbt models, including the ability to review code, reason about data models and metrics, and diagnose pipeline or data-quality issues.
Experience with modern cloud data platforms and BI/semantic-layer tools, with the technical depth to contribute directly when needed rather than managing solely through others.
Demonstrated experience using and promoting AI-assisted software or data engineering approaches, including coding assistants, agents, automated testing, code review, documentation, or workflow automation.
Strong understanding of data architecture, pipeline design, data modeling, governance, observability, access controls, and data-quality practices.
Proven ability to communicate data strategy, trade-offs, and analytical findings to executives, clients, and non-technical stakeholders.
Bachelor’s degree in Analytics, Data Science, Computer Science, Engineering, Healthcare, Business, or a related discipline, or equivalent practical experience.
Advanced SQL proficiency and hands-on experience with dbt or a comparable analytics engineering framework.
Experience with BigQuery or another modern cloud data warehouse; GCP experience is preferred.
Experience with modern BI and semantic-layer tools; Omni Analytics experience is preferred, with Looker also relevant.
Working proficiency with Python or another scripting language for data analysis, automation, and troubleshooting.
Familiarity with Git-based software/data engineering practices, automated testing, code review, observability, and CI/CD concepts.
Practical experience with AI coding assistants, agents, or other AI-native engineering workflows and an ability to establish responsible team-wide practices for their use.
Experience with API- and file-based data exchange patterns, including SFTP, schemas/specifications, recurring batch feeds, reconciliation, and exception handling.

Nice To Haves

Healthcare experience, particularly with health plans, managed care, pharmacy benefits, digital health, population health, or value-based care.
Working knowledge of healthcare datasets and reporting patterns such as eligibility, claims, utilization, care management, clinical outcomes, and population-level analytics.
Experience owning recurring external data exchanges and the operational controls required to keep SFTP, API, or batch-file workflows reliable.
Experience operating in a HIPAA-regulated environment and familiarity with healthcare interoperability standards such as FHIR.
Advanced degree in a relevant discipline.

Responsibilities

Own the vision, strategy, roadmap, and operating model for Data, Analytics & Insights, aligned to company priorities, client commitments, and product strategy.
Lead, coach, and develop a team spanning data engineering and analytics/insights while remaining an active technical contributor who can step into the work when needed.
Partner with Engineering leadership on data architecture, pipelines, modeling standards, testing, observability, platform reliability, and the evolution of canonical data models.
Drive adoption of AI-assisted and agentic engineering practices across Data in partnership with Engineering leadership, establishing practical tools, patterns, guardrails, and quality expectations.
Own the modern data and BI ecosystem, including BigQuery, dbt, and Omni Analytics, and drive trusted self-service analytics through governed datasets, permissions, documentation, and training.
Define and govern metrics, KPIs, semantic models, dashboards, and analytical approaches used to measure member engagement, clinical outcomes, utilization, operational performance, and program value.
Lead and support client-facing analytics, including recurring reporting, Quarterly Business Reviews, outcomes narratives, and ROI/value discussions with healthcare partners.
Own recurring client and partner data exchange, including eligibility/member files, claims and utilization data, source-of-truth files, reporting specifications, SFTP/API feeds, and recurring reports.
Establish monitoring, reconciliation, exception management, lineage/provenance, data-quality controls, and clear ownership so data is accurate, timely, traceable, and trusted.
Serve as a Data Custodian and partner with Privacy, Security, Compliance, and Engineering to maintain appropriate governance and least-privilege access in a HIPAA-regulated environment.
Represent Data in cross-functional roadmap and prioritization processes, translating business needs into clear initiatives, dependencies, sequencing, and trade-offs.
Reduce manual reporting and operational work through automation, reusable data products, standardized processes, and self-service capabilities.
Communicate roadmap progress, risks, blockers, data-quality issues, and decisions required to senior management with clarity and appropriate business context.