CFA-trained analyst turning messy data into clear decisions.
SQL · Power BI · Excel · Financial Modelling.
3+ years building frameworks, presenting insights to stakeholders, and asking
the right business questions.
I'm a Business Analyst with a finance-first mindset. I work at the intersection of business, data, and decision-making.
For me, analytics is not about building dashboards for the sake of visuals — it’s about finding the reason behind business performance. If customer retention drops, I investigate behavioral patterns. If revenue dips despite stable order volume, I look for operational leakage. If cancellations spike, I quantify the business impact and identify process gaps.
My toolkit includes SQL, PostgreSQL, Power BI, DAX, Excel, and Financial Modelling — but the real value I bring is translating data into strategic decisions stakeholders can act on.
I’ve also taught 4,000+ graduates in foundational finance & analytical thinking, trained 100+ CFA L1 aspirants and conducted finance and marketing workshops for graduates as lead speaker giving me strong communication skills alongside technical capability.
I focus on delivering insights that are clear, actionable, and grounded in business context.
SQL · PostgreSQL
Power BI · DAX
Excel · Pivot Tables
Requirements Analysis
Financial Modelling
Stakeholder Management
DCF · Equity Research
Process Mapping
BRD
FRD
Scrum
Agile
CFA Level I — ClearedCFA Institute
MBA in Finance Manipal University · 2026
Data AnalyticsCertified
// 02
Featured Projects
04
01 / 04
ExcelBusiness CaseInsights Presentation
Drone Delivery Feasibility — Taclo
Financial model + BA requirements document + phased roadmap
End-to-end feasibility study for replacing delivery partners with drones at a food-tech startup. Defined success metrics, built a cost model, and proposed a phased implementation roadmap with BA requirements documentation.
27,024/month per drone vs ₹28,438 for delivery partner breakeven at 404 deliveries/month at ₹66.91 each
BA requirements document: stakeholder map, functional requirements, assumptions, out-of-scope
Phased rollout covering tier 1 cities first with hub placement and risk matrix
02 / 04
Full-stack analytics on 99,441 real Brazilian e-commerce orders. 33 SQL queries, 6 reusable views, 1 Power BI dashboard, and a 10-slide insight deck with actual query outputs.
SP + RJ + MG = 63.1% of all revenue — geographic concentration risk
Zero repeat customers — critical retention gap identified
73.9% credit card penetration; Boleto 19% = non-digital user segment
Used CTEs, LAG(), ROW_NUMBER(), CASE WHEN, timestamp arithmetic
Operations analysis for a Bengaluru quick-commerce platform. 22,823 raw orders analysed across time slots, delivery areas, acquisition channels, and monthly revenue trends.
HSR Layout = 69.6% of all orders (15,657 of 22,505) — extreme geographic concentration
September = peak at 4,231 orders — 45% above August
Late Night = only 7% of orders — significant idle capacity cost identified
04 / 04
Multi-dimensional Power BI dashboard on e-commerce sales performance built directly from raw data. State-wise analysis, seasonal trends, delayed delivery by category, product rating analysis.
16M revenue · 99K orders · Avg 4.0 product rating
Auto category = 811 delayed orders — highest delay concentration
Q2 seasonal spike to ₹4.8M identified; Q4 sharp revenue decline
YoY growth 2016→2017→2018 visualised with trend analysis
Project 01 · Excel · Business Case · BA Requirements
Drone Delivery Feasibility Study — Taclo
End-to-end business case for drone-based delivery at a food-tech startup · Self-initiated
₹27,024Monthly cost per drone
404Breakeven deliveries/month
₹66.91Breakeven cost/delivery
~30.6%EBITDA MARGIN (year 1)
Business Problem
Taclo, a food-tech startup, needed to evaluate whether deploying delivery drones could replace or supplement human delivery partners. The goal was to assess financial viability, operational feasibility, and implementation approach — not just at surface level, but with a model that could withstand CFO-level questioning.
What I Built
01
Built a ₹103L NPV Drone Delivery Financial Model
Developed a full financial feasibility model comparing rider vs drone delivery economics. Identified a ₹21.9 per-order cost advantage for drones, 404 orders/month break-even, and 11-month fleet payback with projected ₹102.8 Lakhs 5-year NPV.
02
Designed Formula-Driven Scenario & Sensitivity Analysis
Built dynamic Bear, Base, and Bull case simulations analysing 300–800 monthly orders, ₹25K–₹32K drone operating costs, regulatory delays, and fleet utilisation. The base case achieved ~63% EBITDA margins with ₹45/order delivery cost.
03
Planned Multi-City Drone Rollout Strategy
Created a phased expansion roadmap from Bangalore pilot to Hyderabad, Delhi, and Mumbai deployment. Forecasted scaling from 20 to 55 drones with ₹284.75 Lakhs estimated 3-year investment across fleet, hubs, tech infrastructure, and training.
04
Created Executive Go / No-Go Investment Framework
Designed a weighted decision matrix evaluating ROI, scalability, operational feasibility, technology readiness, ESG impact, and regulatory risk. The model achieved a 3.85/5 investment score, leading to a recommended phased rollout strategy.
Key Finding
At ₹27,024/month per drone vs ₹28,438 for a delivery partner, drones achieve cost parity at 404 deliveries/month (₹66.91/delivery). In a dense zone like HSR Layout or Indiranagar, this is achievable within 3 months of deployment. The recommendation: hybrid model — drones for short-radius peak-hour orders, riders for long-distance/heavy/fragile.
Project Documents
Taclo Drone BA Portfolio PDF
Taclo Financial Model (Excel)
Project 02 · SQL · PostgreSQL · Power BI
E-Commerce Analytics — Brazilian Olist
100,441 orders · 6 relational tables · 33 SQL queries · 6 views · 1 Power BI dashboard
99,441Total orders
63.1%Revenue from 3 states
0Repeat customers
12.5 daysAvg delivery time
SQL Skills Demonstrated
CTEs
Common Table Expressions
Used for MoM revenue growth (monthly_revenue → revenue_growth → final SELECT), customer segmentation, and top-3-per-state ranking logic.
WINDOW
Window Functions
ROW_NUMBER() OVER PARTITION for top customers per state. LAG() for month-on-month revenue growth calculation across 25 months of data.
JOINS
Multi-table Joins
6-table joins across orders, customers, order_items, sellers, products, payments. Handled NULLs and empty strings robustly with NULLIF.
SP + RJ + MG = 63.1% of all revenue — geographic concentration risk. SP alone = ₹5.2M of ~₹13.6M total. Zero repeat customers detected across 99,441 orders — every customer appears exactly once. This is a critical business finding: Olist has no retention mechanism and is entirely dependent on new customer acquisition for growth. 73.9% credit card penetration with Boleto at 19% signals a significant non-digital user segment that needs offline payment support.
15,657 of 22,505 completed orders came from HSR Layout. Fresco is operationally a single-zone business. ITI Layout is a distant #2 at 17.5% (3,946 orders).
02
September surge: 4,231 orders
September = 18.8% of full-year orders. Revenue jumped from ₹11.44L (Aug) to ₹13.22L (Sep). Steady MoM growth from Jan 1,606 → Sep 4,231.
03
Late Night = 7% demand, 20% capacity
Only 1,589 orders during Late Night vs 5,924 in Afternoon. Maintaining full fleet for 7% demand is a margin drain. Reduced-fleet model recommended.
04
Jun revenue dip despite stable orders
Jun orders: 2,628 (stable). But discounts spiked to ₹18,285 vs ₹6,986 in Apr. Discount-driven orders diluted revenue — promotion strategy needs revision.
Fresco Dashboard Summary
Fresco Excel Model
Project 04 · Power BI · DAX · Data Modelling
Sales & Performance Dashboard — Shop-Nest
Multi-dimensional e-commerce analysis · Power BI built directly from raw data · 2024
16MTotal revenue
99KTotal orders
4.0Avg product rating
328Auto category delays
Dashboard Coverage
SALES
State-wise Revenue Analysis
SP dominates at ₹6M+. State-level filter connected to all visuals. Revenue by state visualised as ranked bar chart with cross-filtering.
OPS
Delayed Orders by Category
Auto = 811 delayed orders — highest concentration. Table shows category, delay count, filterable by state and year. Ops actionable.
TREND
Seasonal & YoY Analysis
Q2 revenue spike to ₹4.8M identified. YoY growth 2016→2017→2018 shows platform maturation. Q4 decline visible across years.
RATINGS
Product Rating Analysis
Avg rating 4.0 across categories. Product-level rating bar chart shows which categories underperform. Connected to payment and delivery filters.
