Who we are

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy — a future where mundane repetition disappears and being known unlocks access, comfort, and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn’t coming; it’s here, and we need builders, innovators, and problem solvers to help us create it.

Who you are

Metropolis is seeking a Senior Data Program Manager to embed within our Data Platform and Data Science organization. In this role, you will deeply engage with technical teams to drive complex, cross-functional programs from inception to delivery across data infrastructure and machine learning systems. You will partner directly with engineers, data scientists, analytics engineers, and business stakeholders to bring clarity, structure, and momentum to critical initiatives ranging from data platform migrations to AI-powered product launches. As the connective tissue of our data organization, you will build trusted relationships, streamline operational rigor, and ensure our most ambitious programs deliver predictably and seamlessly.

What you’ll do 

• Own end-to-end program management for multi-team data platform and data science initiatives from scoping and planning through launch and retrospective  
• Define program roadmaps, milestones, and delivery timelines while facilitating flexible agile ceremonies tailored to team needs  
• Proactively identify blockers, surface risks early, and track program health through concise status updates, dashboards, and executive summaries  
• Develop a deep understanding of the data platform stack, including pipelines, cloud data warehouses, dbt models, orchestration tools, and ML infrastructure, to coordinate migrations and upgrades with minimal disruption  
• Support Data Science teams across the machine learning project lifecycle from data preparation and experimentation to model deployment and monitoring