Director, Business Intelligence

Metropolis•Los Angeles, CA

2d•$190,000 - $260,000•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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