Senior Manager Corporate Finance

Delhivery; Dadri; On-site; INR 1,000,000 - 2,000,000; Full time

Job summary

Delhivery in Dadri, UP seeks a data analytics professional who bridges finance and data to drive cost, revenue, and P&L decisions. You will convert large datasets into actionable insights and automate analyses for ongoing reporting.

You will build SQL queries at scale, automate processes with Python, and create dashboards and periodic mailers for Ops and leadership, ensuring data quality end-to-end.

Responsibilities

• Turn large operational datasets into actionable P&L insights across cost, revenue, and reporting.
• Analyze unit economics (cost/kg, margin by facility, lane, vendor) and separate levers from mix effects.
• Support account-level gross margin reviews, segment profitability, and pricing insights.
• Write and optimise large-scale SQL (Trino/Presto) queries on shipment, billing, and fleet data.
• Automate recurring analyses with Python (pandas, openpyxl) producing stakeholder-ready outputs.
• Develop dashboards (QuickSight) and automated mailers for regional and ops teams.
• Build MoM/WoW variance bridges and model scenarios like fleet sizing and EV adoption.
• Present crisp findings with deck-ready commentary and actionable recommendations.

Job description

Education:

B.Tech / B.E. or quantitative degree from a Tier-1 institute (preferred)

About the role

You will sit where finance meets data: you'll 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'll 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

• Exposure to logistics, supply chain or other unit‑economics‑driven businesses
• Comfort using AI tools (Claude, Copilot) to speed up coding and analysis