Senior Analyst - Finance Business Insights

Role: Senior Analyst- Business Insights (Finance) 

Location: Bangalore

Who are we and What do we do? 

InMobi Group’s mission is to power intelligent, mobile-first experiences for enterprises and consumers. Its businesses across advertising, marketing, data, and content platforms are shaping consumer experience in a world of connected devices. InMobi Group has been recognized on both the 2018 and 2019 CNBC Disruptor 50 list and as one of Fast Company’s 2018 World’s Most Innovative Companies. 

What’s the InMobi family like?    

Consistently featured among the “Great Places to Work” in India since 2017, our culture is our true north, enabling us to think big, solve complex challenges and grow with new opportunities. InMobians are passionate and driven, creative and fun-loving, take ownership and are results focused. We invite you to free yourself, dream big and chase your passion.

What do we promise? 

We offer an opportunity to have an immediate impact on the company and our products. The work that you shall do will be mission critical for InMobi and will be critical for optimizing tech operations, working with highly capable and ambitious peer groups. At InMobi, you get food for your body, soul, and mind with daily meals, gym, and yoga classes, cutting-edge training, and tools, cocktails at drink cart Thursdays and fun at work on Funky Fridays. We even promise to let you bring your kids and pets to work. 

Why should you join us? 

You will contribute to creating disruptive and innovative consumer experiences using technology. We value autonomy, collaboration, technical innovation, and results-oriented thinking. InMobi’s culture is all about rewarding excellence so there are fantastic opportunities for the right candidates!

Finance BI team is part of FP&A and is responsible to upscale the organization towards data driven strategic decision making by adding the intelligence layer on top of this data - to unlock the huge potential that the right analytics could provide. This intelligence layer will be consumed towards unlocking productivity, realizing efficiencies, improving RoI for our various Products.

What You Will Do

Own finance data end to end

• 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.

Analyze and find the signal

• 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.

Work independently and drive outcomes

• 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 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.

Good 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.

Key Traits

• Ownership: You take a problem from vague request to working solution without waiting to be told how.
• Curiosity: You explore data, find patterns in messy datasets, and ask why numbers move.
• Pragmatism: You choose the simplest solution that is reliable, maintainable, and actually used.
• Collaboration: You align Finance, Product, and Tech stakeholders on priorities and trade-offs.
• Bias for results: Strategy only matters once it turns into outcomes.