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Most guides about getting into a big company are written for software engineers. This one is for a data engineering role: the person who moves data from where it is created to where people can use it. It explains what Flipkart interviews usually look like, what to practise, and how to spend 8 weeks. You do not need to know any of this already.
Campus drives and referrals are common routes for freshers.
SQL and coding at an easy to medium level.
SQL, coding, data modelling and a project discussion.
A deeper conversation, often with a senior engineer or manager.
Feedback is compared and a decision is shared.
What the role looks like
Flipkart is an Indian e-commerce company, so its data is about products, orders, payments, deliveries and returns. Data engineers build the pipelines and tables that sales, supply and finance teams use every day.
Large sale events bring sudden spikes in traffic and orders. A good interview answer for a pipeline question often mentions what happens when the data suddenly becomes much larger than usual.
How the hiring process usually runs
Read this as a typical order, not a promise. Some stages are skipped for some roles, and for students the first stage is often an internship application.
- Application, campus drive or referral. Students often reach interviews through campus placements or referrals. Keep your resume to one page and name the tools you used.
- Online or phone screen. Easy to medium coding or SQL. Practise under a timer so you finish calmly.
- Technical rounds. SQL, coding and data modelling, plus questions about a project you built. Be ready to explain every part of it.
- Final round. A broader conversation about how you think, how you learn and how you would handle a data problem.
- Decision. The team compares feedback and responds. Timing varies by role.
What they test and how to practise it
Each row is one thing an interviewer wants to see, what to practise for it, and where to practise it here.
| What they check | What to practise | Practise it |
|---|---|---|
| SQL | Joins, GROUP BY, windows, returns and refunds logic | SQL practice |
| Python | Dictionaries, lists, strings, simple file processing | Python practice |
| Data modelling | Orders, order items, products and customers | Primary key vs foreign key |
| Big data basics | Spark, partitions, skew when one seller dominates | Data skew explained |
| Project story | A 5-minute walk through one pipeline you built | Projects |
Tools and ideas worth knowing by name
Large Indian e-commerce companies commonly use open source big data tools. These are the names worth knowing.
- Apache Spark processes large data across many machines.
- Apache Kafka carries streams of events, such as orders and clicks.
- Apache Airflow schedules and monitors pipelines.
- Hive or a lakehouse table format lets you run SQL over large files.
You do not need to be an expert in any of these. You need to say what each one is for, in one sentence, and show that you can learn it.
Questions to practise
These are the shapes of question to practise, written the way an interviewer might say them. They are not a leaked list. Solve each one yourself first, then check your answer.
- For each category, return the top 3 products by sales this month.
- Find customers who ordered more than once in the same week.
- Calculate the return rate per seller.
- An orders table and an order items table are joined and the revenue doubles. What went wrong?
- Find the second highest salary in each department.
- One seller has far more orders than any other and a Spark job is slow. Why, and what can you do?
Practise with orders data
Make a small set of tables: customers, products, orders and order items. Write ten questions: revenue per category, repeat customers, products never sold, returns per seller. Pay special attention to the join between orders and order items. Joining at the wrong level is a classic way to count revenue twice, and interviewers like to ask about it.
An 8-week plan
Study about an hour a day. If you have less time, stretch each row and keep the order.
| Weeks | Focus | What you can do at the end |
|---|---|---|
| 1 to 2 | SQL basics: joins, GROUP BY, NULL | Answer easy SQL questions without looking anything up |
| 3 to 4 | Window functions, CTEs, deduplication | Solve "top N per group" and "latest row per key" questions |
| 5 | Python: dictionaries, loops, strings, files | Solve easy coding questions in about 20 minutes |
| 6 | Pipelines and warehouse ideas | Explain raw, clean and reporting layers and how a rerun stays safe |
| 7 | One project, written up | Walk through the project in 5 minutes and answer "what went wrong?" |
| 8 | Mock interviews for Flipkart | Talk through a problem out loud while you solve it |
Mistakes freshers make
- Memorising answers. Interviewers change the data and the wording. Learn why a solution works, not what it looks like.
- Going silent. Say what you are thinking. A wrong answer with clear thinking often scores better than a right answer nobody understood.
- Skipping the checks. After you write a query, say how you would check it: count the rows, look for NULLs, look for duplicates.
- Having no project. One finished project you can explain beats five half-finished ones.
- Not asking questions. If a question is unclear, ask about the data and what one row means. That is what a working data engineer does.
How to talk about your project
Pick one project and prepare a 5-minute story: the problem, the data, the design, one thing that went wrong, and what you would change. Interviewers at any company, including Flipkart, tend to spend the most time on "what went wrong", because it shows how you think. Build one from the projects page and write it up with a README.
Where to start today
Open the SQL practice problems, solve three, and write down what confused you. Then pick the Learn track that covers it. A small start today is worth more than a perfect plan next month.