Senior Product Manager, Lumina Data Platform

RealPage, Inc.•Richardson, TX

1d•$94,700 - $161,300

About The Position

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.

Requirements

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.
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 Haves

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.

Responsibilities

Data sourcing strategy — prioritize and manage the pipeline of internal and third-party data sources to onboard; weigh coverage, cost, licensing, quality, and effort to decide what gets ingested and in what order.
Source onboarding — define the intake process for new sources, from evaluation and vendor/provider assessment through ingestion, validation, and go-live; partner with data engineering on connectors, schemas, and SLAs.
Data contracts & standards — establish the schema, freshness, quality, and metadata standards that every source must meet, and the governance for changing them without breaking consumers.
Consumer management — own the relationship with the internal teams that consume the platform; run intake for their requests, maintain a clear prioritization framework, and keep a feedback loop that turns their needs into roadmap.
Adoption & reliability — define and track the metrics that matter — source coverage, data freshness and quality, consumer adoption, and time-to-onboard a new consumer — and act on them.
Roadmap & stakeholders — maintain the platform roadmap, communicate trade-offs and timelines, and align sourcing investments with the priorities of the teams you serve.
AI-forward delivery — use AI tools as a default part of how the work gets done — accelerating source evaluation, requirements, documentation, and consumer support — and champion AI-assisted workflows across the platform and its consumer teams.