Employer description & source details
Act as SME for finance data sources, definitions, and business logic across FP&A, Accounting, Revenue, and Business Units. Design and build Python and Spark (PySpark) pipelines that ingest, transform, and reconcile data from ERP, billing, ad platform, and operational systems into a governed analytics layer. Model data for finance use cases (P&L, unit economics, forecasting inputs, variance analysis, close reporting) with clear lineage and documented logic. Connect disconnected datasets across businesses and tools to create a single version of truth. Apply statistical methods to explain performance: regression, time-series forecasting, variance decomposition, hypothesis testing, and anomaly detection. Explore large, messy datasets to find patterns, explain why numbers move, and turn those findings into recommendations leaders can act on. Validate models and metrics rigorously, including back-testing forecasts and quantifying uncertainty. Automate reporting and decision support Replace manual reporting with automated, scheduled, and tested data solutions. Build interactive dashboards and metrics layers in Rill and Power BI that summarize performance and surface recommended actions, not just charts. Define metrics once in a governed semantic layer so every dashboard reports the same numbers. Build AI agents and intelligent workflows Design and deploy automations, reporting and insight generation using agents and workflows for tasks such as commentary drafting, anomaly explanation, reconciliation checks, and ad hoc data Q&A. Identify where agents create real leverage and where simpler automation is the better answer. Scope requirements directly with stakeholders, make reasonable trade-offs, and deliver without step-by-step direction. Own the full lifecycle: technical design
Extracted skills: python, go, sql, machine learning, power bi, finance, operations