Sr Analyst - Finance Business Insights

Role


Responsibilities

• Act as SME for finance data sources, definitions, and business logic across FPA, 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 (PL, 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.
• 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.
• 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 QA.
• 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, build, testing, documentation, deployment, monitoring, and issue resolution.
• Write modular, reusable, version-controlled code that others can maintain.

Tool Stack

• Python: data processing, automation, statistical analysis, and LLM integration
• Spark / PySpark: large-scale transformation (on Databricks or equivalent)
• SQL: modeling and analysis
• Rill: metrics views and operational dashboards
• Power BI: executive reporting
• Claude / LLM APIs: agent and workflow development
• Git: version control

What We Are Looking For

Must-have

• 2 to 5 years of experience in analytics, data engineering, or finance systems.
• Python: 2+ years, including writing tested, modular code for data pipelines and automation.
• Spark / PySpark: 2+ years of hands-on experience building production jobs on Databricks or a comparable platform.
• SQL: Strong, with experience modeling data for analytics and finance use cases.
• Rill or a comparable BI-as-code tool (Looker LookML, Cube, or similar): 1+ year

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.