Remote Data Engineer Jobs in India: Where to Find Them

TITLES = ["Data Engineer", "Junior Data Engineer", "ETL Developer",
          "Analytics Engineer", "Big Data Engineer", "Data Platform Engineer"]
TOOLS = ["Spark", "Airflow", "dbt", "SQL"]
MODES = ["remote", "work from home", "hybrid"]

queries = [f'"{t}" {m}' for t in TITLES for m in MODES]
queries += [f'"Data Engineer" {tool} remote' for tool in TOOLS]
for q in queries[:10]:
    print(q)
print("total queries:", len(queries))

import csv

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

# pip install apache-airflow  (Airflow 2.x)
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator

def extract(): print("download raw file")
def transform(): print("clean and model data")
def check(): print("run quality checks")

with DAG(
    dag_id="daily_sales_pipeline",
    start_date=datetime(2026, 11, 1),
    schedule="@daily",
    catchup=False,
    default_args={"retries": 2, "retry_delay": timedelta(minutes=5)},
) as dag:
    t1 = PythonOperator(task_id="extract", python_callable=extract)
    t2 = PythonOperator(task_id="transform", python_callable=transform)
    t3 = PythonOperator(task_id="quality_check", python_callable=check)
    t1 >> t2 >> t3

import duckdb

con = duckdb.connect()
con.execute("""CREATE TABLE sales AS SELECT * FROM (VALUES
    (1, '2026-11-05', 500), (2, '2026-11-05', 300), (2, '2026-11-05', 300)
) t(order_id, order_date, amount)""")

dups = con.execute("""SELECT order_id, COUNT(*) FROM sales
                      GROUP BY order_id HAVING COUNT(*) > 1""").fetchall()
if dups:
    raise SystemExit(f"Quality check failed: duplicate order_id {dups}")
print("checks passed")

RED_FLAGS = {
    "fee": "Asks you to pay for job, training or equipment",
    "no_tech": "No technical interview",
    "free_mail": "Recruiter uses only a free email domain",
    "rush": "Pressure to accept immediately",
    "vague": "Cannot describe the team or data stack",
}
offer = {"fee": False, "no_tech": True, "free_mail": True, "rush": False, "vague": True}
found = [RED_FLAGS[k] for k, v in offer.items() if v]
print(f"{len(found)} red flag(s)")
for f in found:
    print(" -", f)

Questions to ask before accepting a remote DE role

1. Which data stack does the team use (warehouse, orchestrator)?
2. Who would I report to, and how often do we meet?
3. Is the role fully remote, hybrid or remote within a region?
4. What would my first project be?
5. How are on-call or pipeline failures handled?
6. When will I receive a written offer?

Mon  Check alerts; apply to 2-3 new matches
Tue  Portfolio: improve one pipeline or add a test
Wed  Check 10 company career pages
Thu  Apply to 2-3 matches with tailored resumes
Fri  Community: answer a question or review a PR
Sat  Practise SQL and pipeline design questions aloud
Sun  Rest

Hi <Name>, I saw the remote Data Engineer opening on your
careers page. I have built a daily pipeline with Airflow,
DuckDB and automated quality checks, documented here:
<repo link>. I would be glad to apply and learn more
about your data stack. Thank you for your time.

[ ] Company verified through its official website
[ ] Recruiter identity verified
[ ] Technical interview completed
[ ] Written offer with role, pay and remote rules
[ ] No fees requested
[ ] Laptop, access and on-call rules clear