Microsoft Data Engineer Interview Questions & Practice

Microsoft Data Engineer interviews mix solid SQL, Python and Azure data-stack design (Data Factory, Synapse, Fabric). The auto-graded challenges below cover the SQL and coding rounds.

What to emphasise for Microsoft

Microsoft works on enterprise cloud and Azure data services, so it is worth going in strong on SQL depth, data warehouse modelling, and integration across services — enterprise contexts tend to reward governance, security and maintainability as much as raw throughput. Treat that as where to spend your last few days of revision, not as a script — the fundamentals below are what actually get tested, and a candidate who can write clean SQL and explain a pipeline end to end does well regardless of the company. Note that interview formats change frequently and vary by team and level, so use this as preparation guidance rather than a guarantee of what you will be asked.

What a Data Engineer interview usually covers

Most Data Engineering loops test four things, in some combination. First, SQL — almost always the deepest-tested skill, with joins, aggregation, window functions and top-N-per-group questions appearing again and again. Second, data modelling and pipeline design: how you would ingest a source, where you would clean it, how you would handle late or duplicate records, and how the tables would be laid out for consumption. Third, coding in Python or PySpark, usually a transformation rather than a puzzle. Fourth, a discussion of something you have actually built, where the interviewer probes your decisions and what you would change.

How to practise so it sticks

Reading solutions creates a false sense of readiness. Write the query yourself, run it, and check the output against the expected result — that feedback loop is what turns recognition into recall. Work in patterns rather than one-off puzzles: once you can write a top-N-per-group query from memory, a whole family of questions becomes routine. Then practise explaining your approach out loud before you type, because most interviews assess your reasoning as much as your final answer. Finish by rehearsing two or three projects in enough detail to discuss the trade-offs you made.

A four-week preparation plan

Week one: rebuild SQL fundamentals — joins, grouping, subqueries and CTEs — until they are automatic. Week two: window functions and the analytics patterns built on them (running totals, ranking, period-over-period, deduplication). Week three: PySpark — the DataFrame API, joins and shuffles, partitioning, and why a job is slow. Week four: system-style design questions and behavioural preparation, plus timed mixed practice so you are used to switching between question types. Throughout, keep a short list of the mistakes you actually make and re-test yourself on those, since that is where the marginal gains are.

Practice challenges

• Count Employees Per Department (sql, easy, +50 XP)
• HR: Average Salary by Department (sql, easy, +50 XP)
• Healthcare: Total Fees Collected Per Doctor (sql, easy, +50 XP)
• ROW_NUMBER: Find the Highest-Paid Employee in Each Department (sql, easy, +50 XP)
• RANK: Rank Employees by Salary (With Gaps) (sql, easy, +50 XP)
• RANK vs DENSE_RANK: See the Difference Side by Side (sql, easy, +50 XP)
• SUM OVER PARTITION BY: Running Total Per Department (sql, easy, +50 XP)
• LEAD: Show Next Day's Sales Amount (sql, easy, +50 XP)
• FIRST_VALUE: Show Highest Salary in Each Department for Every Employee (sql, easy, +50 XP)
• LAST_VALUE: Show Last Day's Sales on Every Row (sql, easy, +50 XP)
• Count Total Orders Placed by Each Customer (sql, easy, +50 XP)
• Find the Most Recent Order Date for Each Customer (sql, easy, +50 XP)
• Count Employees Per Department Including Empty Departments (sql, easy, +50 XP)
• Departments With More Than 2 Employees (HAVING) (sql, easy, +50 XP)
• List Departments That Have No Employees (sql, easy, +50 XP)
• Count Employees Hired in Each Year (sql, easy, +50 XP)
• Word Frequency Counter (python, medium, +50 XP)
• Find Employees Who Earn More Than Their Department Average (sql, medium, +100 XP)
• Departments With Many Employees AND High Average Salary (sql, medium, +100 XP)
• Median Salary per Department (sql, hard, +180 XP)

What should I prepare for a Microsoft Data Engineer interview?

SQL joins/window functions, Python data manipulation, and Azure pipeline design (ADF, Synapse, Fabric). Behavioral rounds follow the growth-mindset framework.

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