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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 Microsoft interviews usually look like, what to practise, and how to spend 8 weeks. You do not need to know any of this already.
A referral from someone who works there can help your resume get read.
Background, interests and the role.
SQL, coding and data questions.
Deeper technical and behavioural questions.
The interviewers compare notes and decide.
What the role looks like
Microsoft data engineers build the pipelines and tables behind products and internal reports. Many roles use Microsoft's own cloud, Azure, so it helps to know its main data services by name.
Microsoft talks publicly about a growth mindset, which means treating mistakes and feedback as ways to learn. Interviewers may ask how you handled a hard problem or feedback, so prepare an honest example.
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 or referral. Apply through the careers site. If you know someone who works there, ask politely for a referral. Keep the resume to one page.
- Recruiter conversation. A call about your background, what you want to do and which team might fit.
- Technical interviews. Usually SQL and coding, sometimes questions about how you would design a small data flow. Think out loud.
- Final round. A few interviews with more depth. Expect a question about how you work with others and how you learn.
- Decision. Feedback is collected and compared. The timing varies.
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, aggregates, window functions, NULL handling | SQL practice |
| Python | Lists, dictionaries, strings, small data tasks | Python practice |
| Data modelling | Facts, dimensions, keys, what one row means | Primary key vs foreign key |
| Cloud basics | What storage, a warehouse and an orchestrator do | Learn: cloud track |
| Learning mindset | A story where you learned from a mistake | Write it down and practise it aloud |
Tools and ideas worth knowing by name
Azure is the platform many Microsoft data roles are built on. Know these names and what each is for.
- Azure Data Lake Storage holds large files, such as raw data and Parquet.
- Azure Data Factory moves data between systems on a schedule.
- Azure Synapse Analytics is a place to run SQL analytics over large data.
- Azure Databricks runs Apache Spark for big transformations.
- Event Hubs carries streaming events, similar in idea to Kafka.
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.
- Find customers whose total spend is above the average spend.
- For each department, return the highest paid employee.
- Calculate a running total of sales by day.
- Remove duplicate rows from a table and keep the earliest one.
- Reverse the words in a sentence without using a built-in reverse.
- Describe how you would load a file into a table every night and avoid loading it twice.
Show how you learn
Choose one thing you taught yourself, such as SQL window functions or a Python library. Say how you started, what confused you, what helped, and what you can do now that you could not before. This answers the learning questions that data engineering interviews often include.
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 Microsoft | 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 Microsoft, 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.