We are building a cloud-native data platform that ingests, governs, and serves data to product and analytics teams across the company. We are looking for a Senior Product Manager to own two tightly connected sides of that platform: the sourcing of data into the platform, and the experience of the internal teams who consume it. You will decide which data sources we onboard and how, define the contracts and standards that keep that data trustworthy, and treat the platform's internal consumers — product engineering, analytics, data science, Agentic AI, and operations teams — as your primary customers.
This is a high-leverage role. The quality, coverage, and usability of the platform directly shapes what dozens of downstream teams can ship. You will translate ambiguous, cross-functional needs into a clear roadmap, and you will be measured on adoption, data reliability, and the speed at which internal teams can build on what you provide.
What You'll Own
What You'll Do Day to Day
Interview and shadow internal consumer teams to understand how they use data and where the platform slows them down.
Write clear requirements and data contracts, and partner with engineering to size and sequence the work.
Evaluate new data sources — internal systems and external providers — and make the call on what to onboard.
Triage and prioritize the consumer request queue against a transparent framework.
Report on platform health and adoption to leadership and to the teams who depend on you.
Reach for AI to move faster — drafting specs and docs, summarizing consumer feedback, prototyping, and profiling new data sources — and share what works with the team.
What We're Looking For
5+ years in product management, with meaningful time on data, platform, infrastructure, or API products serving internal or technical users.
Data platform fluency — comfortable reasoning about data pipelines, warehouses/lakes, schemas, data quality, and governance; you can hold a credible technical conversation with data engineers.
Cloud-native context — familiarity with modern cloud data stacks (e.g., object storage, managed warehouses, streaming/ELT, orchestration) and the trade-offs they carry.
Internal-customer orientation — a track record of treating internal teams as real customers, managing their demand, and driving measurable adoption.
Prioritization under ambiguity — you can turn a noisy set of cross-team requests into a defensible, communicated roadmap.
Strong communication — you write clearly, align stakeholders, and make trade-offs explicit.
AI-forward mindset — you actively use AI tools to do your best work and raise your team's leverage, and you stay current as the tooling evolves.
Nice to Have
Real estate / proptech experience — familiarity with property, leasing, or real estate data is desired but not required.
Experience defining data contracts
Familiarity with data catalog, lineage, or self-service analytics tooling.
SQL fluency and comfort exploring data directly.
How We'll Measure Success
Growth in the number and quality of data sources available on the platform.
Data freshness and quality against agreed SLAs.
Adoption by internal consumer teams and reduction in time-to-onboard a new consumer.
Consumer satisfaction and a shrinking backlog of unmet data needs.
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