Microsoft Data Engineer Interview Questions + Guide 2025

With over 228,000 employees worldwide and a reported revenue of $245 billion, Microsoft is among the top companies in revenue generation and employability. Microsoft, while primarily known for its Windows operating system and Bing search platform, is increasing AI and cloud computing efforts with Azure and Copilot. With these expansions, Microsoft data engineers and other data-based professionals have become critical to its development and business strategies.

In this article, we’ll discuss your responsibilities as a data engineer at Microsoft, what kinds of questions you may expect in the interview, how to answer them, and tips to ace the interview like a champ.

Microsoft data engineers design, build, and maintain data solutions. They transform raw data into structured formats for analytics. Using Azure services like Data Factory, Synapse Analytics, and Databricks, they create secure and efficient data pipelines. Their goal is to ensure data is clean, reliable, and optimized for storage and processing. They work with SQL, Python, and Scala to build modern data warehouses, big data solutions, and lakehouse architectures.

Beyond building pipelines, they refine data to meet business needs. They automate deployments using CI/CD pipelines and monitor data systems for performance. Security and governance are key to ensuring compliance and efficiency in large-scale operations.

As a Microsoft data engineer, you will debug ETL pipelines, manage SQL and NoSQL data stores, and automate workflows. You’ll clean messy data, integrate external data sources, and build tools to streamline processing. Your work will support analytics teams, AI-driven insights, and smooth data operations.

The Microsoft Data Engineer interview process is thorough, structured, and focused on evaluating technical skills and cultural fit. Here’s an overview of what you can typically expect:

HR Screening Round

The process usually starts with an HR screening call where the recruiter briefly discusses the role and your background. They’ll assess your interest in the position, basic qualifications, and availability. This call is less technical and more focused on your interest in the position and availability. Prepare for behavioral questions regarding your experience in previous data roles and collaborative expertise in the field.

Hiring Manager Round

The next step is typically an interview with the hiring manager. This round revolves around your experience, particularly your previous work in data engineering. Expect questions about the projects you’ve worked on, your understanding of data systems (like Azure, SQL, and ETL processes), and how you solved data-related problems. This is also an opportunity to discuss Microsoft’s company culture and team dynamics.

Technical Assessment or Take-Home Assignment

The technical assessment is a significant component of the interview process, and it may be a take-home assignment or a live coding interview. Take-home assignments often involve designing data pipelines or solving complex data engineering problems using SQL or Python. Live coding rounds typically involve solving real-world problems on the spot, testing both coding skills and familiarity with Microsoft’s Azure tools.

Behavioral and Cultural Fit

Microsoft places a strong emphasis on cultural fit and team dynamics. During the behavioral interview, you can expect situational questions that explore your problem-solving skills, how you work in teams, and whether your values align with Microsoft’s. They will also likely ask situational questions to understand how you’ve demonstrated leadership, problem-solving, and teamwork in previous roles.

HR Interview and Offer

Finally, after successful technical and behavioral rounds, you’ll have an HR interview. This round focuses on compensation, role expectations, benefits, and final clarifications. If your responses satisfy the expectations of the hiring team, they’ll make you an offer, and you’ll enter salary negotiations.

Microsoft’s data engineering interview questions primarily focus on practical skills in data manipulation, query optimization, algorithm design, and problem-solving in real-world data engineering scenarios. The questions are designed to test technical expertise, analytical thinking, and the ability to apply knowledge in practical situations relevant to Microsoft’s data engineering challenges.

Now, let’s focus on practicing common questions asked in Microsoft data engineer interviews. We’ve gathered a few of them here:

Microsoft Data Engineer SQL Questions

SQL questions test your ability to write efficient, scalable queries for large datasets. Microsoft looks for strong problem-solving skills and an understanding of performance optimization.

1. Let’s say we have a table with an id and name fields. The table holds over 100 million rows, and we want to sample a random row in the table without throttling the database. Write a query to randomly sample a row from this table.

Use ORDER BY NEWID() for small datasets, TABLESAMPLE() for large tables, or a combination of TOP 1 with RAND() for better efficiency.

2. Write a query to get the current salary for each employee.

Use JOIN with the latest timestamp or WHERE with a subquery to filter the most recent salary entry.

3. Write a query to return the number repeated by one times its own value.

Use SELECT number * number FROM table to multiply the value by itself.

4. Write a query to report the sum of regular salaries, overtime pay, and total compensations for each role.

Use SUM() with GROUP BY role, ensuring correct column selection for aggregations.

5. Given tables employees, employee_projects, and projects, find the 3 lowest-paid employees that have completed at least 2 projects.

Use HAVING COUNT(project_id) >= 2, then ORDER BY salary ASC with LIMIT 3.

Microsoft Data Engineer Python Questions

Python questions evaluate your ability to manipulate data, work with algorithms, and implement analytical functions. Microsoft values efficiency, readability, and real-world problem-solving.

6. Given a string, find the length of the largest palindrome that can be made from the characters in the string. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward, ignoring spaces, punctuation, and capitalization.

Use collections.Counter() to count character frequencies and determine the longest symmetric arrangement.

7. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

Use TP, FP, FN calculations with precision = TP / (TP + FP) and recall = TP / (TP + FN).

8. Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order.

Use zip() on sentence.split() to generate overlapping word pairs.

9. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1.

Implement binary search with a condition to check which half is sorted.

10. Consider a trip from one city to another that may contain many layovers. Given the list of flights out of order, each with a starting city and end city, write a function plan_trip to reconstruct the path of the trip so the trip tickets are in order.

Treat it as a graph traversal problem, using a hashmap to map departure to destination.

Microsoft Data Engineer Algorithms Questions

Algorithm questions assess your ability to write efficient, scalable code. Microsoft looks for strong logical thinking, data structure knowledge, and optimization skills.

11. Given two sorted lists, write a function to merge them into one sorted list.

Use the two-pointer technique to traverse both lists and insert elements in order. Alternatively, use Python’s heapq.merge() for a built-in efficient approach.

12. Given a list of integers, write a function gcd to find the greatest common denominator between them.

Use math.gcd() with functools.reduce() to iteratively compute the GCD. If implementing manually, use the Euclidean algorithm, which repeatedly subtracts or divides the smaller number from the larger.

13. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array.

Use the sum formula n(n+1)/2 to get the expected sum and subtract the actual sum of the array. For better efficiency, use XOR operations, which eliminate duplicate numbers in O(n) time.

14. Given an integer n, write a function traverse_count to determine the number of paths from the top left corner of an n×n grid to the bottom right. You may only move right or down.

Use dynamic programming, where dp[i][j] = dp[i-1][j] + dp[i][j-1]. Alternatively, use combinatorics with the formula (2n)! / (n! * n!) for optimal calculation.

15. Given n dice, each with m faces, write a function combinational_dice_rolls to dump all possible combinations of dice rolls.

Use recursion to explore all possible outcomes or itertools.product() for a concise approach. To optimize for large inputs, apply memoization to avoid redundant calculations.

Microsoft Data Engineer Machine Learning Questions

ML questions test your understanding of data modeling, feature engineering, and optimization. Microsoft values clear reasoning, practical approaches, and awareness of trade-offs.

16. Let’s say you’re working on keyword bidding optimization. You’re given a dataset with two columns. One column contains the keywords that are being bid against, and the other column contains the price that’s being paid for those keywords. Given this dataset, how would you build a model to bid on a new unseen keyword?

Use TF-IDF or word embeddings (e.g., Word2Vec) to represent keywords numerically. Train a regression model on past bid data and optimize using gradient descent or decision trees.

17. Why would the same machine learning algorithm generate different success rates using the same dataset?

Factors include random weight initialization, hyperparameter choices, and variations in data preprocessing. Additionally, stochastic processes like dropout and batch sampling in neural networks can introduce variability.

18. You work as an ML engineer for a large company. The company wants to implement a machine learning system that utilizes facial recognition to facilitate employee clock-in, clock-out, and access to secure systems. The company also hires temporary contract consultants who need to be able to use the system. How would you design this system?

Use CNNs for feature extraction and embedding comparisons (e.g., FaceNet) to verify identities. Implement additional security measures such as liveness detection to prevent spoofing attacks.

19. Let’s say that you’re training a classification model. How would you combat overfitting when building tree-based models?

Use pruning, limiting maximum tree depth, and increasing training data. Techniques like bagging (random forests) and boosting (XGBoost, LightGBM) help improve generalization by reducing variance.

20. How would you build a model or algorithm to generate respawn locations for an online third-person shooter game like Halo?

Use reinforcement learning to adjust spawn points based on past player deaths, balancing fairness. Incorporate spatial clustering to prevent players from respawning too close to enemies or active combat zones.

Microsoft Data Engineer Behavioral Questions

Microsoft asks behavioral questions to assess problem-solving, collaboration, and adaptability. They want to see how you handle challenges, communicate with stakeholders, and contribute to a team.

21. How would you convey insights and the methods you use to a non-technical audience?

Use simple analogies, avoid jargon, and focus on business impact rather than technical details.

22. Describe a data project you worked on. What were some of the challenges you faced?

Highlight a complex project, explain the technical challenges, and how you overcame them with creative solutions.

23. Tell me about a project in which you had to clean and organize a large dataset.

Discuss dealing with missing values, inconsistencies, and transformations using tools like SQL or Python’s pandas.

24. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Explain how you adjusted your communication style, provided data-driven insights, and ensured goal alignment.

25. What are you looking for in your next job?

Align your answer with Microsoft’s mission, data-driven culture, and opportunities for impact.

26. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

Describe an initiative where you improved efficiency, automated a process, or solved a complex issue proactively.

Microsoft values your ability to think critically, solve complex problems, and bring a collaborative spirit to their teams. Here are some tips to help you feel confident and ready:

Understand Microsoft’s Data Ecosystem

Microsoft’s tools, like Azure Data Factory, Azure Synapse Analytics, Databricks, and Cosmos DB, are central to the data engineering role. Spend time exploring these platforms. For example, learn how to create an ETL pipeline in Azure or how to optimize queries in Synapse. Showing familiarity with Microsoft’s tech stack will set you apart and demonstrate your readiness to contribute from day one.

Master the Fundamentals

The interview will test your core data engineering knowledge, so make sure your basics are solid. Brush up on SQL for complex joins and query optimizations, Python or Scala for data processing, and big data concepts like partitioning, indexing, and sharding. Be prepared to explain how you’ve implemented these in past projects—Microsoft loves hearing real-world examples that showcase expertise.

Practice Problem-Solving Skills

Microsoft’s interview process often includes coding challenges or case studies that mimic real-world scenarios. Practice solving problems related to data pipeline creation, data transformation, and system optimization. Our platform offers data engineering-focused exercises that can help you build confidence. Remember, they’re not just testing your answers—they’re looking at how you approach challenges.

Be Ready for Scenario-Based Questions

Microsoft interviews often include open-ended, scenario-based questions like, “How would you design a data pipeline to process real-time IoT data?” or “How would you troubleshoot a slow-running query in a distributed system?” Don’t panic—break the problem into smaller steps, explain your thought process, and propose a clear solution. Practice showing how you think critically with our AI Interviewer.

Prepare for Behavioral Questions

Expect questions about how you’ve handled challenges in the past. Microsoft focuses on principles like growth mindset, customer obsession, diversity and inclusion, and making an impact. Be ready to discuss times you solved complex problems, managed competing priorities, or dealt with failure.

Use the STAR method (situation, task, action, result) to structure your answers. Be honest—sharing what you learned from setbacks can be just as powerful as discussing successes.

Acing your Microsoft data engineer interview is about more than just technical expertise—it’s about showcasing your problem-solving skills, collaboration, and passion for impact. With preparation, confidence, and a genuine enthusiasm for innovation, you can demonstrate that you’re ready to contribute to Microsoft’s mission. Believe in yourself. All the best!