Data Analyst - SuperProcure

Data Analyst (Junior) — SuperProcure

Location: Kolkata (on-site / hybrid) Experience: 0–2 years Team: Product & Business Analytics Employment type: Full-time Reports to: Analytics Lead

Why this role exists

SuperProcure runs an end-to-end Transportation Management System (TMS) that moves real freight for enterprise manufacturers — spot freight negotiation, dispatch, trip tracking, and freight accounting. Every one of those steps generates data: how long an indent took to get bids, which transporter quoted what, where trucks idled, where a shipper is overpaying.

That data is currently under-used. We need an analyst who will turn it into answers our product, operations, and customer-success teams act on — and into dashboards our enterprise customers see.

This is a hands-on first analytics hire on the junior side. You will not be building slide decks about other people's numbers. You will pull the data yourself.

What you'll actually do

Answer questions with SQL (roughly 50% of your time) Write queries against our production and warehouse tables to answer questions like: which customers' indent-to-award cycle time got worse last month, which transporters consistently underbid then reject, what percentage of trips have complete GPS coverage.

Build and maintain dashboards (25%) Own a small set of recurring dashboards — platform usage, freight savings per customer, module adoption. Keep them correct, documented, and trusted. Investigate when a number looks wrong instead of shipping it.

Support customer-facing reporting (15%) Help produce the savings and performance reports our Customer Success team takes into enterprise QBRs. Accuracy here is non-negotiable; these numbers go to the customer's procurement head.

Data quality and documentation (10%) Flag broken pipelines, missing fields, and duplicate records. Write down what each metric means so the next person doesn't have to re-derive it.

First 90 days — what success looks like

• Day 30: You can independently write joins across our core logistics tables (indents, bids, trips, invoices) and have shipped your first correct ad-hoc analysis.
• Day 60: You own two recurring dashboards end to end and have documented the metric definitions behind them.
• Day 90: You've found and quantified at least one insight nobody asked you for — a cost leak, an adoption gap, or a data quality problem worth fixing.

Must have

• SQL you can actually write, not just recognise. Joins, GROUP BY, window functions, and the judgment to know when your result looks suspicious. This is the single biggest filter in our process.
• Strong Excel / Google Sheets — pivot tables, lookups, and clean structure.
• Clear written communication. You can state a finding in three sentences without hedging, and say plainly what you're unsure about.