Remote Data Analyst Jobs in India: Where to Find Them

TITLES = ["Data Analyst", "Junior Data Analyst", "Business Intelligence Analyst",
          "Reporting Analyst", "Product Analyst", "MIS Analyst"]
TOOLS = ["SQL", "Power BI", "Tableau", "Excel"]
MODES = ["remote", "work from home", "hybrid"]

queries = [f'"{t}" {MODES[0]}' for t in TITLES]
queries += [f'"Data Analyst" {tool} remote' for tool in TOOLS]
for q in queries:
    print(q)

import csv

rows = [
    ["date", "source", "title", "tools", "mode", "status"],
    ["2026-11-06", "LinkedIn", "Junior Data Analyst", "SQL;Power BI", "remote", "applied"],
    ["2026-11-06", "Career page", "Product Analyst", "SQL;Python", "hybrid", "to apply"],
]
with open("analyst_applications.csv", "w", newline="") as f:
    csv.writer(f).writerows(rows)
print("tracking", len(rows) - 1, "roles")

Project: Delivery performance by city (public dataset)

Finding:  On-time delivery is lowest in two cities, mainly on weekends.
Evidence: Weekend on-time rate is clearly below weekday rate in both
          cities (chart 2); other cities show little difference.
Action:   Review weekend staffing with operations in those cities.
Caveat:   Six months of data; no information on order volume per rider.
Links:    Dashboard <link> | SQL <repo link>

-- On-time delivery rate by city and day type
-- Grain: deliveries has one row per delivery
SELECT city,
       CASE WHEN strftime('%w', delivered_at) IN ('0', '6')
            THEN 'weekend' ELSE 'weekday' END AS day_type,
       COUNT(*) AS deliveries,
       ROUND(100.0 * AVG(CASE WHEN delivered_at <= promised_at THEN 1 ELSE 0 END), 1)
         AS on_time_pct
FROM deliveries
WHERE delivered_at >= '2026-04-01'
GROUP BY city, day_type
HAVING COUNT(*) >= 100
ORDER BY city, day_type;

# Case Assignment: Sales Performance

## Key findings
1. Revenue grew mainly from more orders, not larger orders
2. One category declined steadily over the period
3. Repeat customers account for a large share of revenue

## Method
SQL for aggregation (queries attached), Excel pivots for checks,
one Power BI page for visuals.

## Checks
Row counts before and after joins; totals match source file.

## Caveats and next steps
No cost data; suggest checking margin by category next.

RED_FLAGS = {
    "fee": "Asks for money (registration, training, laptop)",
    "no_interview": "Offer without any discussion of skills",
    "free_mail": "Recruiter uses only free email or chat apps",
    "huge_task": "Assignment looks like real unpaid client work",
}
seen = {"fee": True, "no_interview": True, "free_mail": False, "huge_task": False}
flags = [RED_FLAGS[k] for k, v in seen.items() if v]
print("Stop and verify:" if flags else "No red flags")
for f in flags:
    print(" -", f)

Mon  Alerts: 2-3 tailored applications
Tue  Portfolio: write one insight summary
Wed  Check 10 company career pages
Thu  Alerts: 2-3 tailored applications
Fri  Community: answer a question or share a chart
Sat  SQL and case practice aloud
Sun  Rest

Hi <Name>, I saw the remote Data Analyst opening on your
careers page. I recently analysed delivery performance by
city with SQL and Power BI and wrote up the findings here:
<link>. I would be glad to apply and learn more about the
team's work. Thank you for your time.

[ ] Company verified through its official website
[ ] Recruiter identity verified
[ ] Interview or reasonable assignment completed
[ ] Written offer with role, pay and remote rules
[ ] No payment requested at any stage
[ ] Data access, tools and working hours clear