Why Delhi Students Are Choosing Data Engineering in 2026

Demand drivers for data engineering (2026):
- Cloud warehouses (Snowflake, BigQuery)
- Real-time pipelines (Kafka)
- Data quality + governance
- AI needs reliable data
- NCR companies hiring across fintech, ecommerce, edtech

Skills checklist:
□ SQL (advanced)
□ Python (data libraries)
□ Data modeling
□ ETL / ELT pipelines
□ Spark basics
□ Airflow / orchestration
□ Cloud warehouse
□ Git + deployment basics

-- Daily revenue by category
SELECT
  DATE(order_date) AS day,
  category,
  SUM(amount) AS revenue
FROM orders
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY day, category
ORDER BY day DESC, revenue DESC;

# Clean and load a CSV
import pandas as pd

df = pd.read_csv("orders.csv")
df["order_date"] = pd.to_datetime(df["order_date"])
daily = (
    df.groupby([df["order_date"].dt.date, "category"])["amount"]
      .sum()
      .reset_index()
)
daily.to_parquet("daily_revenue.parquet")

Data engineering salary notes:
- SQL + Python alone won't get top pay
- Pipelines + cloud experience matters most
- Projects with real data impress interviewers
- NCR companies compete with global salaries

Interview checklist:
□ Can I write complex SQL (joins, windows)?
□ Can I explain ETL vs ELT?
□ Can I describe a pipeline I built?
□ Do I know Spark basics?
□ Have I used a cloud warehouse?

90-Day Data Engineering plan:
Month 1 — SQL + Python
  - Joins, aggregates, window functions
  - Python basics + pandas
  - 30+ SQL problems

Month 2 — Pipelines
  - ETL vs ELT concepts
  - Build 1 pipeline with Python
  - Airflow basics
  - Write tests for pipeline code

Month 3 — Scale + Cloud
  - Spark DataFrame basics
  - Cloud warehouse setup
  - Deploy a project to cloud
  - Publish on GitHub

Progress tracker:
Month 1: ☐☐☐☐
Month 2: ☐☐☐☐
Month 3: ☐☐☐☐

Wins this week:
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Next week's focus:
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