The biggest gap for freshers in ETL testing interviews is project experience. You can close it by building practice projects on free data, testing them properly and publishing them on GitHub. Here are five ETL testing projects for practice, from easy to advanced, including the banking and insurance projects recruiters in India often ask about.
Why Build ETL Testing Projects?
- Interviewers ask "explain your project". A real practice project gives you an honest, detailed answer.
- You learn SQL and data warehouse concepts by using them.
- A GitHub link on your resume shows proof, not just claims.
Setup (Free)
- Database: PostgreSQL or MySQL (free), or SQLite.
- Python with pandas to load and transform data.
- A SQL client such as DBeaver.
- Git and a GitHub account.
Project 1: Retail Sales Data Warehouse (Beginner)
Data: a public e-commerce dataset, for example the Brazilian e-commerce dataset by Olist on Kaggle (orders, customers, products, payments).
Build: load raw CSVs into staging, then build dim_customer, dim_product, dim_date and fact_sales.
Test: counts, missing records, duplicate keys, orphan foreign keys, total sales reconciliation, and one transformation rule (for example order value = sum of item prices + freight).
-- Reconciliation: staging total vs fact total SELECT (SELECT SUM(price + freight_value) FROM stg.order_items) - (SELECT SUM(sales_amount) FROM dw.fact_sales) AS difference; -- expect 0
Project 2: Banking Transactions (Intermediate)
Data: generate synthetic customers, accounts and 100,000 transactions with Python (never use real customer data).
Rules to test: daily closing balance = opening balance + credits − debits; no transaction on a closed account; amounts in ₹ with 2 decimals; customer SCD Type 2 when address changes.
Defects to plant and find: a duplicate transaction, a debit loaded as credit, a missing day.
Project 3: Insurance Claims (Intermediate)
Data: synthetic policies, policyholders and claims.
Rules to test: a claim must belong to an active policy on the claim date; claim amount ≤ sum insured; claim status flow (registered → approved/rejected → settled); settled amount totals by month.
This is the "insurance project explanation" interviewers like: you can talk about business rules, not just counts.
Project 4: Healthcare Appointments (Intermediate)
Data: a public medical appointments dataset (several are on Kaggle) or synthetic data.
Focus: data quality: invalid ages, appointment before registration, duplicate patients, masked personal fields. See healthcare ETL testing and data quality checks.
Project 5: Automate the Tests With Python (Advanced)
Take any project above and turn your SQL checks into a pytest suite that runs with one command and produces a report.
import pytest CHECKS = { "no_duplicate_customers": "SELECT COUNT(*) FROM (SELECT customer_id FROM dw.dim_customer WHERE is_current='Y' GROUP BY customer_id HAVING COUNT(*)>1) t", "no_orphan_sales": "SELECT COUNT(*) FROM dw.fact_sales f LEFT JOIN dw.dim_customer c ON c.customer_key=f.customer_key WHERE c.customer_key IS NULL", } @pytest.mark.parametrize("name", CHECKS) def test_check(name, db): # db = fixture returning a connection helper assert db.scalar(CHECKS[name]) == 0, name
Framework guide: Python ETL testing framework.
GitHub Project Template
etl-testing-retail-project/ README.md <- problem, data source, architecture, how to run, results data/ <- small sample files only (link to full dataset) sql/ddl/ <- staging and warehouse tables etl/load.py <- your ETL script tests/sql/ <- one .sql file per check tests/test_dw.py <- pytest suite docs/test_cases.xlsx <- test cases and results docs/defects.md <- defects found, with queries and evidence
In the README, list the defects you found and fixed. That section impresses interviewers most.
How to Explain Your Project in Interviews
Be honest that it is a practice project, then describe the source, the transformations, the checks you wrote and one defect you found, in about two minutes. Script and examples: ETL testing real-time project explanation.
More guides for Indian students: ETL full form · ETL testing with example · ETL testing jobs in India · ETL testing course in India · ETL training in Hyderabad, Bangalore & Pune · best ETL testing course on Udemy · ETL testing roadmap · ETL tester vs ETL developer · interview questions for freshers · free ETL testing mock test · SQL for testers · QA to data engineer · ETL testing kya hai (Hindi) · ETL testing notes · work from home ETL jobs
Data warehouse basics: data warehouse concepts · star vs snowflake schema · fact and dimension tables · SCD types · data reconciliation · data warehouse testing · data warehouse interview questions
Frequently Asked Questions
What are good ETL testing projects for practice?
A retail sales data warehouse, banking transactions with balance rules, insurance claims with policy rules, healthcare data quality checks, and a Python automation suite for any of them.
Where can I get data for ETL testing projects?
Public datasets such as those on Kaggle, or synthetic data generated with Python. Never use real customer data.
Should I put ETL testing projects on GitHub?
Yes. Include a clear README, SQL checks, test cases and the defects you found, and keep only small sample data in the repository.
Can a practice project count as experience?
It does not count as work experience, but it gives you real material to discuss in interviews. Always present it honestly as a practice project.
How do I explain a banking ETL testing project?
Describe the sources (accounts, transactions), the warehouse tables, the business rules such as balance calculation, the checks you wrote and a defect you found.
Asim Noaman Lodhi
Certified Google Partner · QA Consultant · 12+ Years IT
QA consultant specializing in ETL testing and data quality. Trained 913+ students to transition into data testing roles through hands-on, real-world instruction.