BBA Graduate to Data Engineer — A Realistic Career Timeline

BBA → Data Engineer: 12-month plan

Month 1: Python basics
Month 2: Python + SQL
Month 3: Advanced SQL + database design
Month 4: Git + Linux basics
Month 5: ETL concepts + Pandas
Month 6: Spark (PySpark)
Month 7: Airflow + orchestration
Month 8: Data warehouse modelling
Month 9: Cloud (AWS or GCP)
Month 10: Project 1 (end-to-end)
Month 11: Project 2 (cloud-based)
Month 12: Portfolio + interviews

Daily: 2-3 hours weekdays, 4-6 hours weekends
Total: ~600-800 hours

Weekly review checklist:

Phase 1 (Month 1-4):
□ Can I write Python functions from scratch?
□ Do I know SQL joins and aggregations?
□ Have I solved 30+ SQL problems?
□ Do I understand Git commits and branches?

Phase 2 (Month 5-8):
□ Have I built an ETL pipeline end-to-end?
□ Can I write PySpark transformations?
□ Do I understand Airflow DAGs?
□ Have I modelled a data warehouse schema?

Phase 3 (Month 9-12):
□ Do I have 2 projects on GitHub?
□ Is one project cloud-deployed?
□ Have I applied to 30+ roles?
□ Can I explain my projects in 2 minutes?

# Simple ETL: API → clean → database
import pandas as pd
import requests
from sqlalchemy import create_engine

def extract():
    response = requests.get("https://api.example.com/data")
    return response.json()

def transform(raw):
    df = pd.DataFrame(raw)
    df = df.dropna()
    df["date"] = pd.to_datetime(df["date"])
    return df

def load(df):
    engine = create_engine("sqlite:///data.db")
    df.to_sql("clean_data", engine, if_exists="replace")

if __name__ == "__main__":
    raw = extract()
    cleaned = transform(raw)
    load(cleaned)
    print(f"Loaded {len(cleaned)} rows")

Every project README should include:

# Project Name

## Problem
What business question does this solve?

## Architecture
- Data source: API / CSV / database
- Transformation: Python / Spark
- Storage: PostgreSQL / S3 / BigQuery
- Orchestration: Airflow / cron

## Setup
How to run this locally (step-by-step)

## Data Model
Tables, columns, relationships

## Results
- Rows processed: 100,000
- Runtime: 12 minutes
- Insights: (any business insight)

## What I'd Improve
- Add monitoring
- Move to cloud
- Add data quality checks

Why this matters:
Recruiters and hiring managers read
READMEs more than code. A clear
README makes your project look 10x
more professional.