Why E-commerce Firms Are Hiring More Data Analysts in 2026

-- A classic e-commerce funnel analysis
-- "Where are users dropping off between view and purchase?"

WITH events AS (
  SELECT
    user_id,
    MAX(CASE WHEN event = 'view_product'   THEN 1 ELSE 0 END) AS viewed,
    MAX(CASE WHEN event = 'add_to_cart'    THEN 1 ELSE 0 END) AS carted,
    MAX(CASE WHEN event = 'checkout_start' THEN 1 ELSE 0 END) AS checkout,
    MAX(CASE WHEN event = 'purchase'       THEN 1 ELSE 0 END) AS purchased
  FROM analytics.events
  WHERE date >= CURRENT_DATE - INTERVAL '7 days'
  GROUP BY user_id
)
SELECT
  COUNT(*) FILTER (WHERE viewed   = 1) AS users_viewed,
  COUNT(*) FILTER (WHERE carted   = 1) AS users_carted,
  COUNT(*) FILTER (WHERE checkout = 1) AS users_checkout,
  COUNT(*) FILTER (WHERE purchased= 1) AS users_purchased
FROM events;

-- Interviewers love this query.
-- If you can explain the drop-off between checkout and
-- purchase, you've already shown analyst instincts.

# Cohort retention analysis in Python — the analyst's
# second-most-common task after SQL

import pandas as pd

df = pd.read_csv("orders.csv", parse_dates=["order_date"])

# First purchase month per user
df["cohort"] = df.groupby("user_id")["order_date"].transform("min").dt.to_period("M")
df["order_month"] = df["order_date"].dt.to_period("M")

# Cohort retention table
cohort = (
    df.groupby(["cohort", "order_month"])["user_id"]
      .nunique()
      .unstack(fill_value=0)
)

# Convert to retention % of initial cohort size
retention = cohort.div(cohort.iloc[:, 0], axis=0) * 100

print(retention.round(1).head())

# Typical follow-up interview questions:
# - "What's a healthy M1 retention for an e-commerce app?"
# - "How would you explain this drop to a marketing team?"
# - "What would you test to improve M1 retention?"

Where e-commerce analyst jobs are concentrated in India

Bengaluru
  - Flipkart, Amazon, Meesho, Myntra, PhonePe, Swiggy Instamart
  - Highest density of e-commerce analytics roles

Gurugram / NCR
  - Nykaa, Zomato, Blinkit, Delhivery, Lenskart, Moglix
  - Strong growth in quick commerce analyst hiring

Mumbai
  - D2C brands (boAt, Mamaearth, Sugar, Wow Skin Science)
  - Nykaa, Zepto HQ roles, Reliance Retail

Hyderabad
  - Amazon, Flipkart support functions, Swiggy, Myntra Tech

Pune / Chennai
  - Smaller but growing analyst teams at D2C and logistics firms

Rule of thumb:
  Product companies + quick commerce + D2C pay best.
  Service companies (analytics BPOs) pay less but hire more freshers.

Career progression — e-commerce analyst

Year 0–1  ·  Analyst / Associate Analyst
  - Own a dashboard or a funnel
  - Learn SQL properly
  - Support senior analysts on experiments

Year 1–3  ·  Data Analyst
  - Own a business area (category, growth, retention)
  - Run experiments end to end
  - Present to business teams directly

Year 3–5  ·  Senior Data Analyst
  - Own multiple areas or a platform
  - Mentor juniors, set metric definitions
  - Design experiments, not just analyse them

Year 5–8  ·  Lead Analyst / Analytics Manager
  - Own the analytics roadmap for a vertical
  - Hire and grow the team
  - Partner with product and business heads

Beyond
  → Head of Analytics
  → Analytics Engineering
  → Data Science
  → Product Management

E-commerce analyst portfolio — checklist

Project 1 · Funnel analysis
  ☐ Pick a public e-commerce dataset (Kaggle, Google Analytics sample)
  ☐ Build a view → cart → checkout → purchase funnel in SQL
  ☐ Show drop-off by channel, device and category
  ☐ Recommend 2–3 concrete improvements
  ☐ Document in a README with charts

Project 2 · Cohort retention
  ☐ Compute M1, M2, M3 retention by signup cohort
  ☐ Segment by acquisition channel or first order value
  ☐ Identify the retention gap and propose a test
  ☐ Publish results with charts

Project 3 · A/B test write-up
  ☐ Pick a realistic experiment (e.g. free shipping over ₹499)
  ☐ Define metric, sample size, hypothesis
  ☐ Simulate results or use a public A/B dataset
  ☐ Write the "decision" recommendation

Across all three
  ☐ Code on GitHub, README explains problem and method
  ☐ Business recommendation is stated, not implied
  ☐ Charts rendered inside notebook or as image files
  ☐ No secrets, no PII in the data

Interview prep — 4 weeks for an e-commerce analyst role

Week 1 · SQL
  ☐ JOINs, GROUP BY, window functions
  ☐ Funnel queries (view → cart → purchase)
  ☐ Cohort queries (first order, retention)
  ☐ Deduplication of duplicate events

Week 2 · Metrics & business
  ☐ GMV, AOV, take rate, CAC, ROAS, LTV
  ☐ Discounts, returns, refunds
  ☐ Category economics
  ☐ Metric definitions — what counts as "active user"?

Week 3 · Experimentation
  ☐ A/B test design, sample size, power
  ☐ Common pitfalls: peeking, novelty effect
  ☐ Interpreting a test result and deciding to ship or not

Week 4 · Case + behavioural
  ☐ "How would you increase M1 retention by 5%?"
  ☐ "Sales dropped 20% last week — investigate"
  ☐ "Which category should we invest in next quarter?"
  ☐ 2–3 mock interviews with a friend or mentor