Director, Data Engineering & Analytics

Who we are

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 Director, Data Platform Engineering to lead and grow our data organization. In this role, you will own the strategy, architecture, and execution of a modern, scalable data platform that serves as a critical foundation for AI-driven analytics, decision-making, and business intelligence across the company. You will directly oversee three high-performing teams — Data Engineering, Analytics Engineering, and BI Development — and work closely with cross-functional leaders in Product, AI/ML, Finance, and Operations to ensure our data platform serves as a strategic differentiator for the business.

What you'll do

● Lead, mentor, and grow high-performing Data Engineering, Analytics Engineering, and BI Development teams while establishing engineering best practices, career ladders, and performance standards

● Define team charters, OKRs, and strategic roadmaps aligned with company-wide goals while hiring, developing, and retaining top engineering talent

● Own the end-to-end vision, architecture, and execution of the data platform from ingestion and storage to transformation, serving, and consumption layers Drive adoption of modern data stack technologies like dbt, Spark, Airflow, Snowflake, BigQuery, Databricks, and Kafka to ensure scalability, reliability, and performance

● Ensure platform governance, documentation, and trust by implementing robust data quality, lineage, observability, and compliance standards in partnership with Security

● Champion AI/ML infrastructure by partnering with Data Science and AI Engineering teams to power real-time and batch insights, accelerate model development, and prepare data for generative AI and traditional ML workflows

● Oversee analytics engineering and BI development to deliver reliable dbt semantic models, self-serve dashboards, embedded analytics, SLAs, and a data-as-a-product mindset