Director, Business Intelligence

Back to jobsNew Director, Business IntelligenceSeattle, Washington, United StatesApplyWho 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, Business Intelligence – Finance to own the vision, strategy, and execution of Metropolis’s Business Intelligence function. You are an organizational architect with a track record of building high-performing, multi-disciplinary data teams from scratch — engineers, data scientists, and analysts — and shaping the data culture of the organizations you’ve led. You operate comfortably at the intersection of C-suite Finance strategy and hands-on quantitative analysis — translating the CFO’s most pressing questions into a multi-year roadmap and the team to deliver it. You are a builder who thrives on complexity across systems, stakeholders, and business cycles, and who never loses sight of what matters most: trustworthy data and intelligence that drives better decisions, faster. What you'll do Own the Finance Business Intelligence strategy by setting the multi-year vision to build, govern, and scale the finance data environment from pipeline architecture to self-serve analytics and board-level reporting Hire and develop the function’s first BIEs, data scientists, and analysts, building toward a high-performing, multi-disciplinary team Evolve Finance analytics from reporting to intelligence by developing predictive modeling, AI-powered anomaly detection, driver-based forecasting, and scenario simulation Serve as the executive-level data partner to the Finance organization, translating strategic priorities into data infrastructure investments Design and govern the intake, prioritization, and delivery framework for all Finance data work to operate as a high-velocity, trusted product team Drive company-wide Finance data governance by establishing policies, standards, and ownership models that make metrics authoritative, discoverable, and auditable Evaluate and select tools, platforms, and integrations for the Finance data stack in partnership with the CTO and Data Platform team What we're looking for 10+ years in Data Engineering, Business Intelligence, Data Science, or Financial Technology, including 4+ years leading data teams and building organizations from the ground up, with a path to managing managers as the team scales Track record of building and scaling a multi-disciplinary data function at a high-growth technology or operations-intensive company Executive presence with fluent data storytelling skills to connect complex quantitative findings directly to business action Technical foundation in the modern Finance data stack (Snowflake, dbt, Airflow, Spark, Looker/Tableau) and cloud platforms (AWS or GCP), alongside statistical modeling and predictive analytics fluency Analytical depth and quantitative rigor in model validation, forecasting accuracy, statistical significance, and hypothesis-driven analysis Proven ability to drive lasting data governance and quality programs across systems and business cycles Track record of building AI/ML-augmented finance analytics including anomaly detection, intelligent forecasting, and automated variance analysis While not required, these are a plus: BS/BA degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field; advanced degree (MBA, MS, or PhD) in a quantitative discipline Experience with ERP/EPM and FP&A planning tools (Oracle, NetSuite, Workday, Anaplan, Adaptive Insights, or Pigment) in a large-scale transformation context; familiarity with scripting and statistical tools beyond SQL — Python, R, or SAS — and comfort evaluating data science work product from senior ICs Fluency in core Finance processes (AP, AR, GL, revenue recognition, close cycles, FP&A) and experience translating strategic Finance priorities into multi-year data roadmaps; experience designing experimentation and measurement frameworks — defining how a team validates its models, tests financial assumptions, and measures forecast accuracy Background in multi-vertical or multi-entity Finance environments (parking, aviation, retail, or similar operational businesses) Track record of building AI/ML-augmented finance analytics — anomaly detection, intelligent forecasting, automated variance analysis 4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the