Senior Data Product Manager (Remote)

ezCater•Boston, MA

12h•Remote

About The Position

ezCater is seeking a Senior Data Product Manager to lead and scale its Enterprise Data Platform. This role will own the long-term product vision, strategy, and roadmap for the data platform, ensuring it supports analytics, data products, machine learning/data science workloads, and AI/natural language experiences. The Senior Data Product Manager will partner closely with engineering, architecture, and other stakeholders to deliver new platform capabilities, including foundations for the Enterprise Data Hub, and to ensure the platform safely and reliably powers AI-enabled analytics and NL experiences. This role also involves close collaboration with the Senior Data Product Manager for Data Product Activation to align platform capabilities with data product design, shipping, and operation, making activation patterns, SLAs, and AI/NL use cases first-class citizens on the platform.

Requirements

5+ years experience as an owner of Data or Analytics Products, with direct Data Product Management experience strongly preferred; experience owning platform- or infrastructure-adjacent data products is a plus.
7+ years working in or directly with Data Engineering, Data Platform, or Analytics teams, ideally in complex, multi-system environments.
Demonstrated success owning end-to-end data or platform products: from problem discovery and requirements through launch, adoption, and measurable business impact, ideally including reliability, cost, or scalability work on a shared data platform.
Deep familiarity with data warehousing and data platforms (e.g., Snowflake, Redshift, BigQuery), data lakes, and ELT patterns.
Experience working with modeling frameworks (e.g., dbt) and metrics/semantic layers that can support NL/AI analytics.
Strong SQL proficiency and comfort exploring data and platform metadata (e.g., logs, cost, usage) yourself to validate requirements, debug issues, and size opportunities.
Experience with BI tools (e.g., Sigma, Tableau, Looker) and how they consume data from platforms, including governance, performance, cost, and how they will participate in AI/NL analytics (e.g., NL features, grounding on Enterprise Data Platform data).
Experience partnering with Data Science and/or ML Engineering teams and supporting ML/DS use cases on a shared data platform (for example, feature computation, training data pipelines, model scoring/serving, and monitoring).
Proven ability to build and execute multi-quarter product plans that align business and engineering priorities, and to make and communicate trade-offs across competing initiatives—ideally in the context of large, multi-team platform initiatives.
Solid project/program delivery skills, including tracking roadmap progress against estimates and team velocity in a Scrum/Agile environment (e.g., Jira, Confluence), and working inside larger cross-functional programs and planning phases.
Excellent communication and stakeholder management skills: able to explain platform and architectural concepts (including AI/NL implications) to non-technical audiences, influence senior leaders, and work seamlessly with Engineering, Architecture, Analytics, Governance, and business stakeholders.
Hands-on experience with AI-assisted analytics or natural-language query tools (for example, Hex, Snowflake Cortex, or similar) to explore warehouse data, validate requirements, and prototype natural-language analytics or self-serve experiences.
A disposition that is friendly, flexible, pragmatic, and curious, with a desire to learn something new every day and to help raise the bar for the broader data, platform, and product teams.
Ability to travel up to 5 days per quarter for Together Weeks, team gatherings and other events, when applicable.

Nice To Haves

Demonstrated ability to design and evaluate natural-language analytics flows—grounding NL answers in governed warehouse data, thinking through guardrails, and partnering with engineering/analytics to measure quality, latency, and trust.
Familiarity with modern AI-powered data platform patterns (vector/search, semantic layers, conversational analytics, or agentic workflows) and how they change expectations for how business users and customers discover and consume data.

Responsibilities

Lead Enterprise Data Platform product strategy in partnership with Engineering.
Define and continuously refine the Enterprise Data Platform vision and product strategy, grounded in company and Enterprise Data pillar goals and business outcomes.
Identify and quantify platform value by partnering with senior stakeholders to understand current-state friction and future-state needs.
Prioritize platform capabilities (e.g., ingestion patterns, promotion paths, observability, cost controls) using data and experimentation.
Partner with Platform Capabilities and Engineering leadership to turn the Enterprise Data Platform and Enterprise Data Hub vision into an actionable product roadmap.
Own and evolve a multi-quarter Data Platform roadmap, balancing foundational work with high-leverage use cases.
Enable ML and Data Science on the platform by ensuring support for model development and deployment needs.
Turn cross-functional needs into reusable platform capabilities and guardrails.
Define clear contracts between platform and activation (e.g., readiness criteria, SLAs/SLOs, semantic and metrics layers, and serving surfaces).
Translate platform needs into clear technical requirements and platform "contracts" for Data Platform and Data Engineering teams.
Make and communicate trade-offs across value, effort, risk, and timing for platform initiatives.
Drive cross-workstream execution for platform initiatives, orchestrating Data Platform, Data Engineering, Analytics, and partner teams.
Ensure alignment on scope, sequencing, and ownership across producing, shaping, and consuming teams.
Own the product "what and why" for the Enterprise Data Platform, while Engineering and Architecture own the "how".
Co-own a healthy delivery flow with a shared Definition of Ready, clear acceptance criteria, and predictable outcomes.
Own the lifecycle of Data Platform capabilities: from value discovery and design, through build and UAT, into launch, iteration, and deprecation.
Ensure shipped capabilities are usable, trusted, observable, cost-effective, and adopted by downstream teams.
Define success metrics (e.g., time-to-ship data products, SLIs/SLOs for platform health, platform utilization and cost, adoption of AI/NL-powered analytics paths).
Lead UAT with key consumers and own documentation, enablement, and change management.
Monitor usage and business impact and adjust the roadmap accordingly.
Become the go-to expert on the Enterprise Data Platform: architecture, capabilities, constraints, and how it supports various use cases.
Resolve questions and inconsistencies by tracing lineage and platform flows, understanding upstream/downstream systems, and working through governance, quality, and ownership issues.

Benefits

Market competitive salary
Stock options
12 paid holidays
Flexible PTO
401K with ezCater match
Health/dental/FSA
Long-term disability insurance
Mental health and family planning resources
Remote-hybrid work from our awesome Boston office OR your home OR a mixture of both home and office
A tremendous amount of responsibility and autonomy
Wicked awesome co-workers
Employee meal program (and many more goodies) when you’re in our office