Senior Data Product Manager

Vantage Risk Companies•New York, NY

2h•$130,000 - $175,000•Hybrid

About The Position

At Vantage, the Senior Data Product Manager is a member of our Data & Analytics team. The mission of the Data & Analytics team is to equip Vantage with the data infrastructure, platforms, and analytical capabilities needed to underwrite smarter, manage risk with precision, and move faster than our competitors. This is a high-ownership, hands-on role. You will manage product roadmaps for key initiatives relying on our internal data platforms — the pipelines, data warehouse, transformation layers, and self-serve tooling that our data engineers, analysts, and data scientists depend on daily. You will work at the intersection of technology and business impact, translating data strategy and requirements into a sequenced roadmap that engineering can execute and stakeholders can trust. The Senior Data Product Manager works closely with data engineering, reporting and business leads to define priorities, author detailed requirements and ensure solutions are adopted and delivering measurable value. This role is a leader in the full lifecycle of business requirements, solution design, build and test, deployment, and rollout. This role reports to the VP of Data Governance & EDW. This is a remote or hybrid opportunity depending on location.

Requirements

Bachelor’s degree in Computer Science, Information Systems or a related field, or equivalent professional experience.
7+ years of experience in product management, with at least 3 years owning data platforms, data infrastructure, or analytics products in a complex organization.
Strong working knowledge of the modern data stack (e.g., dbt, Snowflake, or equivalent tools).
Ability to read and write SQL — not expected to write production queries, but deep enough in the data to QA assumptions and validate outputs.
Track record of shipping products for internal technical customers with measurable, documented outcomes.
Demonstrated ability to build and maintain a product roadmap that engineering teams can execute and business stakeholders can understand.
Experience in insurance, financial services, or another regulated industry.
Actively uses AI tools (e.g., Claude) to accelerate day-to-day product work — writing requirements, synthesizing discovery sessions, preparing stakeholder communications, and analyzing data faster than traditional methods allow.
Experience enabling business users to self-serve on data analysis, including identifying where users (underwriters, actuaries, finance) are stuck waiting on analyst support and championing AI-powered tools that close that gap.
Exposure to AI/ML workflows and an understanding of how model pipelines depend on upstream data quality and platform reliability.
Excellent listening, interpersonal, written, and oral communication skills; able to translate between technical and business audiences without losing nuance.
Independently motivated and self-directed; able to exercise independent judgment and act upon it in a fast-moving, startup-oriented environment.
Continuous learner, coachable, and open to feedback and trying new approaches.
Ability to effectively prioritize and execute under pressure while maintaining attention to detail and quality.
Expert collaborator who can build strong relationships within the Data team and with partners in other parts of the business.

Responsibilities

Own and maintain the roadmap for key data initiatives including ingestion pipelines, transformation layers, data warehousing, and self-serve analytics.
Translate business priorities and data team pain points into a sequenced, outcome-oriented roadmap with clear success metrics for every initiative.
Define and track platform KPIs — story sizing, adoption rates, data quality and delivery reliability — and use them to drive roadmap decisions.
Communicate roadmap direction and delivery status to both technical and business stakeholders with clarity and consistency.
Conduct continuous discovery with internal customers — analysts, underwriters, business leads, actuaries, data engineers — to surface and prioritize unmet data needs.
Write crisp problem statements, user stories, and acceptance criteria that give engineering teams the clarity to execute without over-specifying the solution approach.
Identify and eliminate friction in how the data team produces, consumes, and governs data across the organization.
Elicit, analyze, and validate requirements across the full data lifecycle: sourcing, ingestion, transformation, storage, access, and consumption.
Partner with data engineering to plan and deliver platform improvements in agile cadence; own sprint priorities, remove blockers, and make scoping trade-offs with clear rationale.
Coordinate go-live plans including documentation, training, and rollout sequencing for internal users.
Manage and track requirements throughout the delivery lifecycle, ensuring changes are reflected in sprint planning and communicated to stakeholders.
Ensure new platform capabilities are transitioned to BAU operational processes and that adoption follows delivery.