Sr. Data Engineer Interview questions at Atlassian

As someone who has recently interviewed for the Sr. Data Engineer position at Atlassian, I want to provide a detailed breakdown of the interview process, share the types of questions I faced, and offer insights to help you prepare effectively. This role focuses on building and optimizing data infrastructure, working with large datasets, and ensuring that the data is accessible and usable for decision-making. Here’s an overview of the process and what you can expect as a candipublishDate for this role.

Interview Process Overview

The Sr. Data Engineer interview process at Atlassian is multi-stage and includes a variety of technical and behavioral assessments. The process is structured to evaluate your technical proficiency in data engineering, problem-solving abilities, and your cultural fit within the company. Below is a step-by-step breakdown:

1. Recruiter Call (Initial Screening)

• Duration: 30-45 minutes
• Purpose: The recruiter call is the first step, where they assess your background and understand your motivations for applying to Atlassian. They’ll provide you with an overview of the role, the team structure, and Atlassian’s culture.

Key Questions:

• “Tell me about your background in data engineering and what excites you about the Sr. Data Engineer role at Atlassian?”

• “What questions do you have working with cloud platforms like AWS, GCP, or Azure?”

• “Why Atlassian? How do you see yourself fitting into our team and culture?”

• Preparation Tip: Prepare to discuss your questions with data pipelines, cloud technologies, and big data tools like Apache Spark, Kafka, or Airflow. Be clear about your interest in Atlassian’s mission and how you can contribute to building scalable and efficient data systems.

2. Technical Screening (Data Engineering and SQL)

• Duration: 1 hour
• Purpose: This round tests your technical knowledge, particularly your ability to work with large datasets, SQL, and ETL processes. Expect to answer questions that require you to write SQL queries or solve data-related challenges.

Example Questions:

• “Write an SQL query to calculate the moving average of monthly active users over the last 6 months.”

• “Given a dataset of transactions, how would you identify and handle duplicate records?”

• “How do you design a data pipeline that ingests data from multiple sources and loads it into a data warehouse?”

• “Explain how you would use Apache Kafka to stream data in real-time for a service like Jira.”

• Preparation Tip: Brush up on advanced SQL techniques, including window functions, subqueries, and joins. Be comfortable discussing ETL frameworks and data quality issues, such as handling missing values or detecting anomalies in large datasets. Also, review cloud-based data warehouses like Redshift, BigQuery, or Snowflake, as they are frequently used in modern data architectures.

3. System Design Interview (Data Architecture and Scalability)

• Duration: 1-1.5 hours
• Purpose: In this round, you will be asked to design a data system or architecture that can handle large-scale data processing or storage. The goal is to assess your ability to build scalable and robust data infrastructure.

Example Scenarios:

• “Design a real-time data pipeline to process events from Atlassian products, ensuring low latency and high availability.”

• “How would you architect a data lake that stores structured and unstructured data for analysis? What considerations would you make to ensure it scales as data grows?”

• “Describe how you would optimize a slow-running data pipeline. What tools and techniques would you use to improve performance?”

• Preparation Tip: Focus on designing systems that are scalable, resilient, and efficient. Be prepared to discuss data partitioning, sharding, and replication. Also, practice explaining your decisions, such as why you would use Apache Kafka for real-time data streaming or Airflow for managing complex workflows.

4. Behavioral Interview (Collaboration and Problem-Solving)

• Duration: 1 hour
• Purpose: This round assesses your collaboration skills, leadership potential, and how you approach problem-solving in a team setting. Atlassian values strong team players, so you’ll be evaluated on how well you interact with others and manage cross-functional projects.

Key Questions:

• “Tell me about a time when you led a data engineering project that involved multiple teams. How did you ensure the project was delivered on time?”

• “Describe a situation where you faced a technical challenge with data processing or storage. How did you overcome it?”

• “How do you prioritize tasks when working on multiple data engineering projects at once?”

• Preparation Tip: Be ready to talk about your questions managing data engineering projects, especially those that required you to work with cross-functional teams like product managers, data scientists, and business analysts. Use the STAR method (Situation, Task, Action, Result) to structure your answers and focus on how you resolved challenges and delivered results.

5. Final Interview with Senior Leadership (Strategic Fit and Vision)

• Duration: 45 minutes to 1 hour
• Purpose: The final interview typically focuses on your long-term vision, strategic thinking, and alignment with Atlassian’s values. Senior leadership will assess how you can contribute to Atlassian’s growth through data-driven insights and how well you will fit into their team-oriented, collaborative culture.

Key Questions:

• “Where do you see the future of data engineering in the next 5 years? How do you plan to stay ahead of emerging trends?”

• “How do you ensure that the data infrastructure you build can support the evolving needs of the business?”

• “What is your approach to mentoring junior data engineers and helping them grow?”

• Preparation Tip: Be clear about your vision for data engineering, especially how you can contribute to scalable architectures, and future-proof systems. Atlassian values innovation, so think about how you can contribute to driving technical excellence and mentoring others.

Key Skills Evaluated

1. Technical Proficiency in Data Engineering

Strong skills in SQL, ETL processes, cloud platforms (AWS, GCP, Azure), and questions with data lakes or data warehouses. You should also be proficient in big data tools like Apache Spark, Flink, or Hadoop.

2. System Design and Scalability

Ability to design and scale data systems that support large-scale data processing. This includes building real-time data pipelines, batch processing workflows, and managing large datasets in distributed environments.

3. Collaboration and Leadership

questions working with cross-functional teams and leading data engineering projects. You’ll need to demonstrate how you can mentor others, influence decisions, and work well in a team-oriented environment.

4. Problem-Solving and Business Acumen

You must be able to take on complex data challenges, solve problems efficiently, and communicate the impact of your work in terms of business outcomes.

Preparation Tips

1. Master SQL and Data Engineering Concepts

Practice writing advanced SQL queries and be familiar with ETL frameworks like Apache Airflow and dbt. Review cloud-based data systems like Redshift and BigQuery.

2. Design Scalable Data Systems

Be prepared to discuss data pipeline architectures and how you would optimize them for performance, scalability, and fault tolerance. Consider aspects like data partitioning, sharding, and real-time processing.

3. Strengthen Your Leadership and Collaboration Skills

Be prepared to discuss how you’ve led data engineering projects, worked with cross-functional teams, and mentored junior engineers.

4. Understand Atlassian’s Culture and Values

Atlassian emphasizes collaboration, innovation, and data-driven decision-making. Show how your skills and questions align with these values.