Delhivery - Senior Manager - Corporate Finance

About the role:

You will sit where finance meets data: you will turn large operational datasets into P&L decisions on cost (cost per kg, fleet mix, vendor share), revenue (account and segment profitability) and reporting. You will build the analysis, then automate it so it runs without you.

What you will do:

• Cost and margin analytics: Break down unit economics (cost/kg, cost/shipment, load factor, vehicle utilisation) by facility, lane and vendor type, and separate the levers you can act on from mix effects and noise.
• Revenue and profitability: Support account-level gross margin actioning, segment P&L, and pricing and revenue-leakage reviews.
• SQL at scale: Write and optimise Trino/Presto queries on high-volume shipment, billing and fleet tables, and reconcile the outputs against finance books.
• Python automation: Turn recurring analyses into scripts (pandas, openpyxl) that produce stakeholder-ready Excel and Word outputs, including files too large for Excel.
• Dashboards and mailers: Build QuickSight dashboards and Google Sheets/Apps Script automations, such as scheduled flash mailers to regional and ops teams.
• Variance and scenarios: Build MoM/WoW variance bridges and split movements into volume, rate and mix. Model scenarios such as EV adoption and fleet right-sizing.
• Storytelling: Turn the numbers into crisp, deck-ready commentary for leadership: lead with the "so what" and back it with the number.
• Execution: Coordinate inputs across Ops, Product, BD and Finance, track initiatives to closure, and own data quality end to end.

Must-haves:

• Strong academic pedigree: B.Tech/B.E. or a quantitative degree from a Tier-1 institute (IITs, NITs, BITS, IIITs, DCE/NSUT, ISI, SRCC/St. Stephen's Economics or equivalent). An MBA from IIM/ISB/XLRI/FMS is a plus.
• 2 - 4 years in analytics, strategy, consulting, FP&A or a data role. Experience at a top analytics firm or high-growth consumer/logistics tech company is preferred.
• Strong SQL (joins, window functions, CTEs, performance on large tables). This is non-negotiable.
• Working Python (pandas): you have automated something real, not just done coursework.
• Advanced Excel: financial modelling, pivots, lookups and clean model structure.
• Sharp business logic: you can explain why a metric moved, not just that it moved.
• You reconcile numbers obsessively and catch data errors before they reach a stakeholder.

Good to have:

• QuickSight, Tableau or Power BI; Google Apps Script
• Exposure to logistics, supply chain or other unit-economics-driven businesses
• Finance basics: P&L structure, gross margin, revenue recognition (IndAS 115 a plus)
• Comfort using AI tools (Claude, Copilot) to speed up coding and analysis