Senior Analyst - Finance Business Insights

Overview

Senior Analyst in the Finance BI team (part of FP&A) to build the intelligence layer for data-driven strategic decision making, owning finance data end to end and automating reporting and insight generation.

What you'll do

• Own finance data end to end and 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 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.
• Define metrics once in a governed semantic layer so every dashboard reports the same numbers.
• Design and deploy agents and workflows for commentary drafting, anomaly explanation, reconciliation checks, and ad hoc data Q&A, and identify 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.

What you'll need

• 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 of hands-on use, or a demonstrated ability to become productive quickly.
• Power BI: Working knowledge of data modeling and DAX (or equivalent).
• Statistics: Applied experience with regression, time-series forecasting, hypothesis testing, and anomaly detection.
• ETL/ELT and lakehouse experience: Hands-on with tools such as Databricks, dbt, or Airflow, and familiarity with warehouse and lakehouse architecture.
• LLM APIs: Hands-on experience building with LLM APIs (Claude preferred), including prompt design, tool use, structured outputs, and evaluation.
• Finance fundamentals: Solid understanding of accounting, financial statements, and FP&A concepts. Able to build and check financial models.
• End-to-end delivery: A track record of translating a business problem into a sustainable data product and working independently as the go-to person for a domain.
• Communication: Able to document decisions, write clear specs, and explain results to non-technical leaders.
• Bachelor's or master's in engineering, Computer Science, Statistics, Mathematics, Finance, or a related field.

Nice to have

• Experience with agent frameworks (MCP, LangGraph, or similar) and multi-step AI workflow orchestration.
• Databricks certification (Data Engineer Associate or Professional) or an equivalent cloud data certification.
• Exposure to ad tech, digital media, or high-volume transactional businesses.

Details

• Location: Bangalore.