Atlassian Interview Questions, Values Interview & Hiring Process

Atlassian builds the tools that sit quietly at the center of modern teamwork. Jira and Confluence power agile planning and documentation for hundreds of thousands of companies, while Trello, Bitbucket, and newer products like Jira Service Management and Loom help teams ship software, track work, and communicate more effectively. Atlassian serves customers in more than 200 countries and continues to expand its cloud platform with AI-powered features that live directly inside Jira, Confluence, and Trello.

If you are preparing for an Atlassian interview, this guide will walk you through what to expect. You will learn why candidates target Atlassian across engineering, analytics, product, and business roles, how the interview stages work from first application to hiring committee, and the types of questions that appear again and again.

Use it as your roadmap to show strong craft skills, clear communication, and genuine alignment with Atlassian’s values. If you want to compare Atlassian’s interview style with other companies, you can browse patterns in the companies directory.

Why Work At Atlassian? Culture, Products, And Values

Atlassian sits in a sweet spot between product-led growth and enterprise scale. Teams use Jira to run sprints, Confluence to capture decisions, Trello to organize projects, and Bitbucket to manage code. Features roll out across a large cloud customer base, so an improvement to how teams plan epics or resolve incidents can quickly change behavior at scale.

Candidates are usually drawn in by three things: the problem space, the culture, and the values.

Products with real leverage

• Atlassian tools support software development, IT service management, and general work management, which means your work affects how other teams work every day.
• Because the products are used across startups and global enterprises, your features often reach many different types of teams and industries.

Values that actually show up in interviews

Atlassian organizes its culture around five core values:

• Open company, no bullshit. Information is open by default, and honest, thoughtful communication is expected.
• Build with heart and balance. Teams care deeply about craft, but also weigh impact, cost, and sustainability.
• Don’t #@!% the customer. Customer impact comes first, especially when tradeoffs are uncomfortable.
• Play, as a team. Collaboration, humility, and a bit of fun matter as much as individual brilliance.
• Be the change you seek. Atlassians are expected to improve systems, not just work inside them.

These values are not just marketing copy. They are used explicitly in the values interview and referenced in hiring documentation, performance reviews, and leadership expectations.

A culture of distributed, flexible work

Atlassian’s TEAM Anywhere policy allows many employees to work from almost anywhere in the country where they are hired, with distributed teams across APAC, EMEA, and the Americas. For many candidates, this combination of remote flexibility and global teammates is a major draw, especially for engineering and data roles.

Atlassian’s interview process is built to answer a few simple questions:

• Can you solve real problems in your craft with clear, structured reasoning?
• Do you work effectively with others, especially in a distributed environment?
• Do your decisions and behavior align with Atlassian’s values in real situations?

When candidates search for the Atlassian interview process, they’re usually trying to understand how many stages there are, what kinds of Atlassian interview questions show up in each stage, and how the values interview fits into the overall decision.

The exact sequence varies by role and seniority, but most candidates will see some version of the steps below. When people search for the Atlassian hiring process, what they usually want is this high-level flow: application, an initial craft assessment, a recruiter screen, a virtual interview loop, values and management interviews, then a hiring committee decision. For early career roles, Atlassian describes a flow of application, assessment, then an interview loop that blends craft, leadership, and values-focused conversations.

Atlassian Interview Stages At A Glance

Below is a closer look at each stage.

Application Review

The application review focuses on whether your resume shows clear impact and alignment with the role. Recruiters skim quickly, so formatting your resume around clear, measurable outcomes helps you stand out. You can review sample interview problem patterns in the learning paths to understand what types of experience align well with Atlassian’s roles.

What stands out:

• Quantified outcomes like reduced incident volume, improved deployment speed, or higher adoption of a feature
• Clear descriptions of your role on the project instead of vague team achievements
• Experience that maps to Atlassian’s problem space, such as agile tooling, collaboration software, cloud systems, or analytics for product teams

Tip: Rewrite each bullet as “I improved X metric by Y amount by doing Z.” This mirrors how Atlassian teams talk about impact internally and makes it easier for interviewers to picture you in the role.

Initial Assessment: Coding, Cases, And Take-Homes

For many engineering candidates, Atlassian uses an initial coding screen that covers data structures, algorithms, and sometimes light system design. Other roles see parallel assessments tailored to their craft.

Common patterns:

• A short online coding test for software engineering roles
• A take-home case study for product management, focused on prioritization and customer impact
• A data exercise that asks you to query a dataset, build a simple dashboard, or interpret experiment results
• A written or recorded exercise for business, design, or operations roles

Tip: Treat the assessment as a preview of the loop. Practice solving problems out loud, even when you are alone. Atlassian cares about your reasoning process as much as the final answer.

For engineering and data roles, practicing structured problem solving through the SQL interview learning path and the data science interview learning path helps you build the exact skills these initial screens look for.

Recruiter Screen

The recruiter screen checks for basic fit, expectations, and readiness for the loop.

Expect questions such as:

• Why Atlassian and why this specific product or team
• How your recent experience maps to the role
• Your location, working rights, and timeline
• High-level questions about your preferred tech stack or analytical tools

Recruiters will also explain the upcoming stages, including how many interviews to expect and how the values interview works.

Tip: Prepare a two-minute story that covers who you are, what you have been working on recently, and what you want to do next at Atlassian. This becomes the anchor for the rest of the process.

If this is your first time going through a structured interview cycle, the AI interview tool is helpful for rehearsing your intro, your “why Atlassian” narrative, and quick behavioral responses.

Virtual Interview Loop

Most experienced candidates go through a virtual loop that lasts several hours in total. For engineering roles, the loop often includes data structures and algorithms, code design, system design, and craft-specific interviews for frontend, backend, SRE, or full-stack roles. Non-engineering candidates see parallel structures with case-based conversations instead of coding rounds.

To see how these interviews map to common industry patterns, explore similar question types in the data engineering learning path or by practicing hands-on problems in the challenges library .

Below is a structured table summarizing the major components.

Atlassian Virtual Loop Components

Management And Values Interviews

Near the end of the loop, you will usually meet your hiring manager and a separate interviewer dedicated to values.

• Management interview. Focuses on how you work with others, how you make decisions, and what kind of impact you want to have at Atlassian. Expect to walk through one or two projects end-to-end, including technical or analytical details, stakeholder dynamics, and business results.
• Values interview. Conducted by someone outside your immediate team, often from a completely different function. The goal is to understand how you have lived the five core values in your past work, with lots of behavioral and situational prompts.

Tip: Prepare eight to ten STAR stories that you can map to multiple values. For each one, know the metric, the conflict or tradeoff, what you did personally, and what you would change in hindsight.

Hiring Committee And Decision

After your loop, interviewers submit written feedback which is then reviewed by an independent hiring committee. The committee looks at your technical performance, values alignment, and overall strengths to decide whether you meet Atlassian’s bar for the role and level.

You usually do not interact with this committee directly, and hiring managers typically do not override a committee decision. This structure is meant to create more consistent standards across teams and reduce the influence of any single interviewer.

Tip: If you reach this stage, ask your recruiter what level you are being considered for and how that affects expectations. Understanding the target level helps you interpret the outcome and plan for a potential re-interview window if needed.

Across roles, Atlassian interviews tend to repeat the same core patterns. You will write code or SQL against realistic data, design systems and schemas, reason about metrics and experiments, and show how you work with teams in a values-driven environment. The exact mix varies by role, but the underlying themes stay consistent: clarity, structured thinking, and practical judgment over flashy tricks.

Use the categories below to benchmark your prep and map them to the role you are targeting, whether you are aiming for:

• Atlassian Software Engineer
• Atlassian Product Manager
• Atlassian Machine Learning Engineer
• Atlassian Business Intelligence Engineer
• Atlassian Business Analyst
• Atlassian Data Scientist
• Atlassian Data Engineer
• Atlassian Data Analyst

The examples below blend Atlassian-style questions across engineering, data, analytics, ML, BI, and PM roles. Many are available as interactive problems inside Interview Query if you want to practice with real data and solutions.

Coding and Algorithms Questions

Coding questions appear most often for Atlassian software engineers, machine learning engineers, and data engineers.

Lighter versions also show up for data scientists and data analysts during technical screens.

Atlassian emphasizes clarity, correctness, and reasoning. Interviewers want to see how you structure a solution, justify trade-offs, anticipate edge cases, and communicate constraints. Clean code and simple designs matter more than clever tricks or niche optimizations, whether you are solving a classic array problem or an Atlassian voting system interview question framed around feature requests or issue prioritization.

Sample Coding & Algorithms Questions

Data modeling & pipeline design questions

Data modeling and pipeline design come up most often for software engineers, machine learning engineers, and data engineers, and it also appears in more advanced loops for data scientists and business intelligence engineers.

Atlassian’s design rounds are practical: you are not expected to recite every buzzword, but to frame the problem, lock the grain, choose sensible components, and explain trade-offs. Interviewers look for clear diagrams, structured reasoning, and awareness of data quality, latency, and reliability in an environment where products like Jira and Confluence generate massive event streams.

Sample systems design questions

SQL & Analytics Questions

SQL and analytics questions appear most often for data analysts, business intelligence engineers, data scientists, and data engineers.

These questions test how you query real-world datasets, interpret ambiguous metrics, and communicate insights clearly—crucial in Atlassian’s environment where product decisions depend on accurate dashboards and well-defined metrics.

Atlassian heavily values clean logic, attention to detail, and defensible assumptions. Interviewers expect structured thinking, readable SQL, and the ability to interpret outputs rather than simply write queries.

Sample SQL & analytics questions

Systems Design Questions

Systems design interviews are common for software engineers, machine learning engineers, and data engineers. These interviews focus on how you decompose ambiguous problems, design scalable components, reason about trade-offs, and communicate your thinking clearly.

The goal is not architectural perfection but clarity, practicality, and ability to justify decisions. Interviewers look for how you size the system, define constraints, identify bottlenecks, and prioritize simplicity over over-engineering.

Sample systems design questions

Product Sense & Problem-Solving Questions

Product sense questions appear most frequently for product managers, but they also show up for data scientists, business analysts, and software engineers when the role interacts heavily with product teams. Many candidates think of these as “Jira interview questions” or “Confluence interview questions and answers,” because they’re often framed around improving specific workflows in Jira, Confluence, or Trello. Atlassian uses these questions to understand how you reason about users, break down ambiguous problems, define metrics, and propose practical improvements grounded in customer value.

Expect interviewers to push you toward structured thinking: define the user, identify their pain points, choose the right metric, explore trade-offs, and justify why your solution matters. These aren’t creativity tests—they’re logic, prioritization, and communication tests.

Sample product sense questions

Machine Learning & Modeling Questions

Machine learning and modeling questions most often appear for machine learning engineers, data scientists, and applied scientists, with lighter conceptual variants sometimes asked of software engineers who work on ML-adjacent teams.

These questions assess how you frame prediction problems, select and compare models, reason about noise and bias, evaluate experiments, and communicate ML trade-offs in a practical, product-facing environment like Jira, Confluence, or Trello.

Atlassian interviewers want to see structured intuition, not memorized algorithms: define the problem, explain the baseline, compare approaches, identify failure modes, and tie your choices back to user or business impact.

Sample machine learning & modeling questions

Data & Metric Case Studies

Data and metric case studies appear frequently for data scientists, business analysts, business intelligence engineers, and data analysts.

They also appear in product-facing interviews for product managers and software engineers who work closely with metrics-heavy teams.

These questions test how you frame ambiguous problems, define useful metrics, analyze changes in user behavior, debug unexpected movements, and tie your conclusions back to product and business decisions. Interviewers look for structured reasoning, clear assumptions, and communication that is focused on insight rather than complexity.

Sample data and metric case questions

Atlassian values and behavioral interview questions

Many candidates search specifically for Atlassian values interview questions, core values interview questions, or Atlassian behavioral interview questions. These all point to the same part of the loop: a structured conversation where interviewers test how you have lived Atlassian’s values in real situations, not just how well you can recite them.

Expect questions like:

• “Tell me about a time you disagreed with a stakeholder. How did you handle it?” (Tests “Open company, no bullshit” and “Play, as a team.”)
• “Describe a time you shipped something that didn’t go as planned. What did you do next?” (Tests ownership, learning, and “Be the change you seek.”)
• “Tell me about a time you had to balance speed with quality.” (Tests “Build with heart and balance.”)
• “Have you ever pushed back on something because it wasn’t right for the customer?” (Directly tests “Don’t #@!% the customer.”)

The core pattern behind Atlassian values interview questions is consistent: interviewers want to understand the context, the options you saw, the decision you made, and what you learned. If you can show measurable impact and honest reflection, you will stand out.

Prep tip: Build a short bank of STAR stories that you can flex across multiple Atlassian core values. Practice them out loud using the AI interview tool or mock interviews so your answers feel natural instead of memorized.

Atlassian favors clear reasoning, collaborative problem solving, and decisions rooted in customer value. The frameworks below work across coding, design, analytics, ML, and product interviews.

Clarify → structure → execute

A simple universal pattern:

• Clarify the goal, constraints, and assumptions.

• Structure a plan before diving into details.

• Execute while explaining trade-offs and expected impact.

  This keeps your answers clear, concise, and aligned with Atlassian’s communication style.

Metrics-first thinking

Define success before proposing solutions. Choose one primary metric, two or three guardrails, and relevant segments. This matters especially in analytics, PM, and data interviews supported by the learning paths.

Trade-off narratives

Atlassian values balanced, thoughtful decisions. Present two or three realistic options, compare the benefits and risks, and explain your recommendation. Tie decisions back to reliability, simplicity, or customer experience.

Customer and teammate perspective

Show how your choices improve the experience for the end user and simplify collaboration for teammates working across Jira, Confluence, or Trello. This demonstrates alignment with values like “Don’t #@!% the customer” and “Play, as a team.”

Short, flexible STAR answers

Use tight STAR responses for behavioral prompts, but focus most detail on your Actions and Results. Keep variants of each story ready so they map easily to follow-up questions.

Atlassian interviews reward structured thinking, practical judgment, and strong alignment with the company’s values. These focused preparation steps help you perform consistently across the loop.

Build a compact STAR story bank

Prepare 8 to 10 concise, metric-driven STAR stories that show ownership, collaboration, and customer impact. Keep the Situation to two sentences and emphasize your actions and measurable results. You can strengthen these by practicing behavioral prompts inside the mock interviews platform.

Practice thinking aloud across all problem types

Interviewers evaluate how you reason, not just the final answer. Whether you are preparing for coding, SQL, analytics, or product interviews, practice restating the problem, verbalizing assumptions, outlining your plan, and explaining trade-offs. For structured practice, use the ai interview tool or the sql interview learning path.

Do a mock loop to build endurance

Real loops last several hours and mix different interview formats. Simulate a full loop with one coding or craft round, one design scenario, one metrics or product case, and one behavioral interview. Engineering and data candidates can use the data science learning path or the data engineering learning path to guide practice sessions.

Prepare deeper layers for follow-up questions

Atlassian interviewers dig into details to test authenticity. For each story or technical example, be ready to explain the metric, the alternatives you considered, the risks you managed, and what you would change today. Reviewing problem sets on the takehomes page helps you build this depth.

Craft thoughtful reverse interview questions

Strong reverse questions signal curiosity and alignment with Atlassian’s culture. Ask about team workflows, success metrics, and how distributed teams collaborate. You can explore additional context using the companies directory.

Atlassian offers competitive compensation across engineering, data, and product roles, with total annual packages driven by level, equity, and location. Entry-level roles often start above $150K total compensation, while senior ICs and product leaders can exceed $500K due to strong stock components. Salaries in the United States generally benchmark closely against other cloud-focused, product-led software companies such as Adobe, HubSpot, and Snowflake.

The table below summarizes typical total annual compensation ranges for Atlassian’s key technical and analytical roles, based on publicly available data from Levels.fyi, Glassdoor, and Indeed.

Average Compensation by Role

What these ranges mean for candidates

These figures reflect self-reported salary data, so they should be treated as directional rather than exact. Compensation varies based on level, location, and stock valuation at the time of grant. For most candidates, the biggest drivers of total compensation are:

• Leveling accuracy (small title jumps can create large salary differences)
• Equity grant size (Atlassian is stock-heavy at senior IC and PM levels)
• Location (San Francisco and Austin tend to yield stronger packages)

If you are preparing to negotiate an offer, use Levels.fyi and Indeed ranges as directional benchmarks, not fixed numbers, and confirm the latest bands with your recruiter because compensation ranges and stock valuations change over time.

How competitive is the Atlassian interview process?

Atlassian’s interview process is competitive because the company hires selectively for strong fundamentals, communication skills, and cultural alignment. Candidates usually complete a recruiter screen, a technical or take-home assessment, and three to four interview rounds. Practicing through the ai interview tool or the learning paths helps you build consistency across these stages.

How should I prepare for the technical interviews at Atlassian?

The best preparation strategy is to build structured problem solving. Focus on data structures, algorithms, SQL, analytics, and system reasoning depending on your role. You can use the sql interview learning path, the data science interview learning path, or the data engineering interview learning path to review common patterns and question frameworks.

Does Atlassian ask behavioral questions?

Yes. Behavioral interviews are a core part of the loop and focus on collaboration, ownership, and customer impact. Prepare 8 to 10 STAR stories that demonstrate measurable outcomes and resilience. You can practice these stories in realistic sessions through mock interviews or 1-on-1 coaching.

What take-home or assignment formats should I expect?

Some roles include a take-home case, such as a small analytics project, a data transformation task, or a product thinking exercise. Atlassian expects clarity in assumptions, readable logic, and thoughtful interpretation of results. Reviewing previous cases on the takehomes page helps you understand the typical structure.

Does Atlassian hire internationally or remote?

Yes. Atlassian has a distributed work model with employees across Australia, the United States, India, and other regions. Hiring varies by role, but many engineering and data positions are open to remote or location-flexible candidates. You can explore role availability using the companies directory.

What level should I apply for at Atlassian?

Apply for the level where you meet most of the expectations and can demonstrate scope through past projects. Atlassian calibrates levels carefully, so P30 to P40 fits early career candidates, while P50 and above require demonstrated leadership, cross-functional influence, and measurable business impact. Reviewing compensation ladders in this guide and browsing similar roles through the companies directory can help you estimate your fit.

How important is system design for Atlassian roles?

System design is essential for senior engineering, data, and ML candidates. Expect to discuss tradeoffs, high-level architecture, and how your design supports reliability, scalability, and collaboration. You can build foundations using the data engineering learning path or the modeling and machine learning learning path.

Does Atlassian use take-home SQL or analytics challenges?

Some roles include SQL or analytics exercises that test your ability to clean data, write readable queries, and explain insights clearly. Reviewing patterns in the sql interview learning path or practicing through the challenges library helps you prepare for these tasks efficiently.

Succeeding in an Atlassian interview comes down to clarity, structure, and the ability to think collaboratively under pressure. Whether you are preparing for data roles, engineering positions, or product paths, your strongest advantage is consistent, realistic practice anchored in real interview patterns. Explore targeted study plans in the learning paths, sharpen your technical reasoning with guided challenges, or book time with an expert through mock interviews. Start building your edge now and step into your Atlassian interview with confidence and momentum.