Data Product Manager

Veterinary Emergency Group (VEG)•White Plains, NY

3d•$140,000 - $160,000•Onsite

About The Position

At VEG, we find a way to say YES — to better data, smarter decisions, and care that is always improving. As a Data Platform Product Manager, you'll sit at the center of one of the most important capabilities we're building: the data and analytics infrastructure that helps every corner of VEG make better decisions, faster. This is not a passive coordination role — it's for someone who is innately curious and prides themselves on being strong both technically and strategically. You'll own the vision and roadmap for your data domain, deciding what gets built and why, then work closely with our Data Engineering team to make it happen. You'll translate messy, ambiguous business problems into clear, buildable data products, hold the standard for data quality and documentation, and be the person our business and analytics teams rely on to understand what the data means and how to use it well. You'll be part of a small, high-ownership team with the ear of senior leadership — if you want your fingerprints on decisions that matter and the chance to grow somewhere that invests in its people, this is the role.

Requirements

Strong SQL and genuine comfort in databases and data warehouses (e.g., Snowflake) — you write novel queries, validate joins, investigate anomalies, and spot logic errors independently
Experience with data pipelines, dimensional data models, or semantic layers — you understand what makes raw source data analysis-ready and where it breaks
Proven ability to find the job to be done inside a business problem and translate it into structured data requirements, while managing a roadmap or backlog in a fast-moving environment
Clear, confident communicator — able to explain complex data concepts to non-technical stakeholders and push back on unclear or under-scoped requests with credibility
Early-adopter mindset and excitement for using rapidly evolving AI tools to expand your own and your team's effectiveness
BA/BS or equivalent experience; background in analytics, data engineering, business intelligence, or a related field

Nice To Haves

Empathetic, instinctively taking a people-centric approach, whether supporting your colleagues or making an effort to understand different perspectives
Have a sense of humility; acknowledging mistakes, sharing credit with others, and lifting up your team’s’ accomplishments
Feel a strong sense of ownership over your work, taking responsibility for outcomes and staying committed to achieving long-term, impactful results
Curious by nature; you ask insightful questions and continuously seek out opportunities to learn and grow your skills and knowledge

Responsibilities

Own the data product roadmap. Maintain and prioritize the data engineering backlog for your domain — balancing business impact, technical feasibility, and strategy — and partner with Data Engineering to sequence work, allocate resources, and surface tradeoffs to senior leadership and your core users.
Bridge business needs and technical execution. Serve as the conduit between the teams who need data and the engineers who build it, converting ambiguous requests into clear, scoped specs through use-case discovery: the decision being made, the metric or tool needed, the audience, and what success looks like before any build begins.
Partner with source system owners. Engage owners across HRIS, scheduling, and clinical systems as a thought partner on the configuration and schema decisions that affect downstream data quality and usability.
Own data quality and reliability. Design and run QA to validate new and updated data assets — pressure-testing logic, finding edge cases, confirming behavior across joins and transformations — maintain validation queries and dashboards, and communicate clearly during outages: what's down, why, and when it will be resolved.
Own documentation and knowledge management. Make sure every asset your team builds has clear, accurate documentation — metric definitions, business rules, known limitations, intended use — so data becomes genuinely self-service and questions-per-asset drop over time.
Enable better data use across VEG. Support analytics and business teams in using your domain's data correctly, spot the patterns in where they struggle, and turn those insights into better assets, documentation, and onboarding.