Data Engineer Intern Interview questions at Atlassian (2025 Summer U.S. Program)

As a candipublishDate for the Data Engineer Intern position at Atlassian for the 2025 Summer U.S. Program, I had the opportunity to go through a rigorous but insightful interview process. The internship program at Atlassian is highly competitive, focusing on providing students with hands-on questions in data engineering, big data technologies, and cloud infrastructure while working on real-world projects. Here’s a detailed breakdown of the interview process, including questions, tasks, and tips to help you succeed.

1. Overview of the Role:

As a Data Engineer Intern, you’ll work on building and maintaining data pipelines, processing large datasets, and contributing to data architecture improvements. You will gain exposure to:

• ETL processes (Extract, Transform, Load)
• Working with cloud platforms like AWS or Google Cloud
• Utilizing big data tools such as Hadoop, Spark, and Kafka
• Collaborating with data scientists, data analysts, and engineers to solve complex data problems.

The internship aims to provide learning questionss where you’ll gain practical exposure to data infrastructure, scalable systems, and working within Agile teams.

2. Interview Process:

The interview process for the Data Engineer Intern role at Atlassian is designed to evaluate your technical proficiency, problem-solving skills, and your ability to work in a collaborative environment. Here’s what to expect:

Step 1: Application and Screening (Online)

The first step is the online application, which includes submitting your resume, cover letter, and answering a few questions about your motivation and technical background. This is followed by a recruiter screening call where the recruiter will assess your basic qualifications and interest in the internship.

Common Questions in the Recruiter Screening:

• “What interests you about the Data Engineer Intern role at Atlassian?”
• “Why did you choose data engineering as your career path?”
• “Can you describe any previous questions or projects involving data processing or working with big data technologies?”

Tip: During this call, it’s important to show enthusiasm for Atlassian’s products (such as Jira and Trello) and to demonstrate that you understand the data engineering field. Mention any relevant coursework, personal projects, or internships where you worked with tools like SQL, Python, or data engineering platforms.

Step 2: Technical Phone Screen (1 hour)

The technical phone interview is conducted with a Data Engineer or a Senior Engineer. This is the first deep dive into your technical skills, where you may be asked to solve coding problems related to data manipulation, algorithms, or data structures.

Example Coding Questions:

• SQL Problem: “Write a SQL query to find the second-highest salary from an employee table.”
• Python Problem: “How would you process a large log file to extract meaningful metrics (e.g., errors, user activity, etc.)?”
• Data Structures: “Given a list of integers, write a Python function that sorts the list and removes duplicates.”

Key Focus Areas:

• SQL proficiency: Be prepared to solve queries that involve joins, aggregations, subqueries, and window functions.
• Programming: Strong knowledge of Python (or your language of choice) and understanding how to manipulate data using libraries like Pandas and NumPy.
• Data Structures and Algorithms: You may be asked questions related to sorting, searching, or basic algorithms used to process data efficiently.

Tip: Practice solving coding problems on platforms like LeetCode, HackerRank, or CodeSignal. Focus on data manipulation and using efficient algorithms to handle large datasets.

Step 3: Technical Take-Home Assignment (2-3 hours)

For the next round, you’ll receive a take-home coding assignment. This task assesses your ability to work on a real-world data engineering problem. The assignment typically involves writing code that processes large datasets, extracts information, and stores the results efficiently.

Example Assignment:

You may be given a CSV file with large amounts of data and asked to write a script in Python or SQL that:

• Cleans the data (handling nulls, duplicates, etc.)
• Aggregates the data based on certain criteria (e.g., time-series data)
• Stores the cleaned and processed data in a structured format (e.g., in a SQL database or cloud storage)

Tip: Before starting the assignment, make sure to read the instructions carefully and plan your approach. Break down the task into smaller steps and optimize your code for efficiency, especially when handling large datasets.

Step 4: Final Interview (Behavioral & Problem-Solving, 45-60 minutes)

The final interview typically involves a behavioral interview with a team lead or hiring manager. During this interview, they assess how you would approach real-world problems, your teamwork abilities, and how well you fit into Atlassian’s culture. This round often includes problem-solving scenarios where you’ll need to explain your thought process, decision-making, and collaboration style.

Example Behavioral Questions:

• “Tell me about a time when you worked on a project with a team. What challenges did you face, and how did you overcome them?”
• “How do you ensure your code is well-structured and maintainable?”
• “Describe a situation where you had to work under tight deadlines. How did you manage your time?”

Tip: Use the STAR method (Situation, Task, Action, Result) to structure your answers. Focus on showing that you can work well under pressure, collaborate with others, and are passionate about data engineering and continuous learning.

3. Key Skills and Qualifications Atlassian Looks For:

• Programming Languages: Proficiency in Python or Java for data processing tasks. Knowledge of SQL for querying databases is also essential.
• Data Processing: questions with ETL pipelines, data manipulation, and working with large datasets.
• Data Storage: Familiarity with databases (e.g., MySQL, PostgreSQL, MongoDB) and knowledge of cloud storage solutions like Amazon S3 or Google Cloud Storage.
• Big Data Technologies: Familiarity with tools like Hadoop, Spark, Kafka, and other distributed data systems is a plus.
• Problem-Solving: Strong analytical skills and the ability to break down complex data problems into manageable tasks.
• Collaboration: Ability to work in a cross-functional team and effectively communicate technical results to non-technical stakeholders.

4. Tips for Success:

• Prepare for Technical Coding: Practice coding problems related to data manipulation, SQL queries, and algorithms. Use platforms like LeetCode and HackerRank to improve your problem-solving skills.
• Understand Data Engineering Fundamentals: Review the basic concepts of ETL, data pipelines, and cloud computing. Brush up on technologies like Spark, Hadoop, and AWS if you have time.
• Work on Data Projects: If you have any personal projects that involve processing large datasets or building data pipelines, be ready to discuss them. This can set you apart from other candipublishDates.
• Prepare for Behavioral Questions: Be ready to explain past questionss where you collaborated on projects, overcame challenges, and delivered results. Atlassian places high value on teamwork, communication, and problem-solving.
• Be Clear in Your Communication: When solving technical problems, especially during live coding or the take-home assignment, be sure to explain your thought process clearly. Atlassian values transparency and clear communication.

5. Example Behavioral Questions:

• “Tell me about a time when you had to learn a new tool or technology quickly. How did you approach it?”
• “Describe a situation where you found an issue with the data. How did you handle it?”
• “How do you ensure the quality and accuracy of the data in your projects?”