Atlassian Data Analyst Interview Guide: Questions, Process & Prep

Atlassian builds the tools that power teamwork across the world, from Jira and Confluence to Trello and Bitbucket. With more than 300,000 customers and millions of daily users, every workflow, issue, and collaboration event generates data that shapes how teams ship software and stay aligned. Data analysts sit at the center of this ecosystem, turning complex product, usage, and operational data into insights that guide product strategy, customer experience, and business performance.

If you are preparing for Atlassian data analyst interview questions, this guide helps you understand the role, the interview stages, and the types of questions you can expect across SQL, analytics, experimentation, dashboards, and behavioral themes. Use it as your complete roadmap to preparing for one of the most collaborative and product-focused analytics environments in tech.

What does an Atlassian data analyst do

Atlassian data analysts help product, engineering, design, and go-to-market teams understand how people use tools like Jira, Confluence, Trello, and Bitbucket. They work across product analytics, experimentation, business performance, and customer behavior to drive decisions that improve collaboration and accelerate how teams build software.

Key responsibilities include:

• Analyzing product usage to understand adoption, retention, feature engagement, and friction points
• Partnering with product managers and engineers to design, measure, and interpret experiments
• Building dashboards that track product health, acquisition funnels, team workflows, or customer outcomes
• Defining metrics that standardize how teams measure success across Atlassian’s product suite
• Conducting deep-dive analyses that influence roadmap decisions and inform strategy
• Working with large datasets across Snowflake, SQL, and Atlassian’s internal analytics tools
• Translating insights into clear narratives for product, leadership, and cross-functional stakeholders
• Ensuring data quality through documentation, monitoring, and improvements to underlying data pipelines

Atlassian analysts operate in a highly collaborative environment, where clarity, empathy, and thoughtful communication are just as important as technical excellence.

Why this role at Atlassian

The data analyst role at Atlassian is ideal for someone who wants to work at the intersection of product, teamwork, and analytics. You drive decisions for products used by millions worldwide, shape how customers collaborate, and influence features that support some of the most innovative teams in tech. With strong ownership, modern data infrastructure, and a culture built around clear values, it is a role that combines meaningful impact with long-term growth.

Atlassian’s interview process is designed to understand how you think with data, collaborate with cross-functional teams, and communicate insights with clarity. Rather than focusing solely on technical questions, Atlassian evaluates your ability to structure ambiguity, reason about product behavior, and operate within a values-driven culture.

Here is the general flow of the interview:

Stage	Primary Focus
Recruiter Screen	Communication, background fit, motivation
Technical Assessment	SQL depth, analytical structure
Technical Interviews	Product reasoning, structured problem solving
Behavioral Interviews	Collaboration and values alignment
Final Interview	Strategic thinking and long-term fit

Initial recruiter screen

The recruiter screen sets the tone for the process and is your opportunity to illustrate the scope of your past analytical work. Recruiters listen for clarity when describing your past projects, how you structured ambiguous problems, and how you partnered with teams to deliver insights. This conversation gives them a sense of whether your work aligns with the real product and operational questions Atlassian analysts handle.

They also evaluate motivation and cultural alignment. Strong candidates express genuine interest in Atlassian’s mission, understand the core product ecosystem, and can speak about how they approach collaboration in team-centric environments. This stage helps determine whether moving into deeper technical rounds makes sense for both sides.

Tip: Prepare one concise, measurable example that demonstrates how your analysis changed a decision or improved a product.

Technical assessment

The technical assessment evaluates your real-world analytical skills. Depending on the team, you may receive a SQL test or a short take-home task. Exercises often reflect the type of work analysts do with Jira, Confluence, and Trello data:

• Multi-step SQL questions requiring joins, filtering, and window functions
• Cleaning or normalizing messy usage data
• Interpreting product metrics such as activation, engagement, or retention
• Producing a small visualization accompanied by a brief written explanation
• Explaining reasoning and assumptions when definitions or signals are ambiguous

To prepare for realistic datasets, many candidates practice using the SQL Scenario-Based Interview Questions collection, which closely matches Atlassian’s analytics style.

Tip: Favor clarity in your queries. Well-structured CTEs and readable logic reflect real production work.

Technical interviews

Technical interviews simulate how Atlassian analysts collaborate with PMs, engineers, and designers. You may work through a dataset based on a Jira-like scenario and discuss how you would measure engagement, diagnose a drop in usage, or design an experiment. Interviewers assess how you break down problems, how you communicate assumptions, and how you think about customer behavior through data.

Common focuses include:

• Investigating product anomalies or unexpected trends
• Defining meaningful product or team-level metrics
• Structuring experiments and walking through expected outcomes
• Evaluating funnels, cohorts, retention curves, or segmentation
• Prioritizing which questions to answer first when data is unclear

To simulate these conversations, many candidates use Interview Query’s mock interviews, which mirror Atlassian’s interactive, discussion-based format. Book a mock interview →

Tip: Communicate your thought process as a sequence. Atlassian values analysts who make reasoning transparent.

Behavioral interviews

Behavioral interviews explore how you collaborate and how your working style aligns with Atlassian’s values. These conversations focus on partnership, influence, and how you advocate for customers through data.

Behavioral Theme	What Interviewers Look For
Collaboration	How you work with PMs, engineers, and design partners
Handling Ambiguity	How you move forward with imperfect requirements
Conflict & Influence	How you persuade through evidence, not authority
Customer Focus	How you elevate user experience in your work
Values Alignment	How your behavior reflects Atlassian’s core values

Tip: Choose stories that demonstrate shared problem-solving. Atlassian values analysts who work through challenges with teams, not around them.

Final interview

The final round, usually with a hiring manager or senior leader, focuses on long-term fit and strategic thinking. This stage looks beyond execution and into how you shape direction, prioritize analytical work, and frame insights for product strategy.

You may be asked to:

• Walk through a past project end-to-end, including tradeoffs and impact
• Evaluate a simplified Atlassian product scenario and propose how you would measure success
• Outline how you prioritize analytical questions when supporting a product team
• Discuss how you would balance short-term improvements with long-term customer value
• Reflect on situations where you influenced strategy through data

Tip: Frame your recommendations in terms of user outcomes and product impact. Atlassian’s decisions start with understanding customer value.

Atlassian data analyst interviews focus on how you think with data rather than how many functions you can memorize. Expect a mix of SQL, product analytics reasoning, metric definition, experimentation, and behavioral storytelling. The questions are designed to mirror real Atlassian work: investigating ambiguous product signals, defining meaningful metrics, and interpreting complex usage patterns across Jira, Confluence, Trello, and other products.

Below are the most common categories of questions you will encounter, starting with SQL and data manipulation

SQL and data manipulation interview questions

SQL is central to Atlassian’s data analyst interviews. You’ll work with event data, product usage tables, user-level logs, and team-centric workflows. Interviewers look for correctness, clear logic, and the ability to reason about messy product data.

1. Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of employees earning over 100K.

   Solve this by grouping employees by department, calculating the percentage of those earning above 100K, and filtering to departments with at least ten employees. Use DENSE_RANK() to rank departments by high-earner percentage so ties are handled consistently.

   Tip: Always explain how you validate intermediate outputs; Atlassian wants analysts who verify logic, not just write queries.

2. Calculate first touch attribution for each user who converted.

   Join sessions with conversion events, filter down to converting users, and identify the earliest session using ROW_NUMBER() or MIN(timestamp). Assign the source of that earliest session as the first-touch channel.

   Tip: Mention how you handle timestamp gaps, session edge cases, and incomplete data, which are common in product analytics.

3. Write a query to get the number of customers that were upsold.

   Define upsell behavior clearly, such as moving to a higher-tier plan or increasing order value over time. Group by user, compare behavior across periods or product tiers, and filter down to users matching the upsell criteria.

   Tip: Clarify the business definition before writing SQL; Atlassian values analysts who convert ambiguity into precise logic.

4. Write a query to find the average number of right swipes for different ranking algorithm variants.

   Join swipe events with experiment assignment data, group by algorithm variant, and compute the average number of right swipes per user or per session depending on the grain.

   Tip: State your grain explicitly; mismatched grains cause inconsistent metrics in real product systems.

5. Write a query to get the current salary for each employee after an ETL error duplicated records.

   Use ROW_NUMBER() partitioned by employee and ordered by the most recent timestamp to isolate the correct row. This recovers the current salary while gracefully handling duplicated entries.

   Tip: Tie your answer to data quality practices; Atlassian values analysts who think about root-cause prevention.

6. Write a query to randomly sample a row from a 100M-row table without throttling the database.

   Use a random ID strategy or a probabilistic sampling filter instead of ordering the entire table by randomness. This avoids expensive full-table operations on large datasets.

   Tip: Highlight performance awareness; efficient reasoning matters at Atlassian’s scale.

7. Return all neighborhoods that have zero users.

   Perform a left join from neighborhoods to users and filter where the user fields are null. This reliably identifies neighborhoods without associated users.

   Tip: Explain why left joins are preferred for absence checks; this shows strong fundamentals in relational reasoning.

   Try this question yourself on the Interview Query dashboard. You can run SQL queries, review real solutions, and see how your results compare with other candidates using AI-driven feedback.

To build stronger SQL intuition before the interview, you can practice on Interview Query’s curated sets such as the SQL Questions for Data Analysts or walk through scenario based problems in the SQL Scenario Based Interview Guide. These sets mirror the style and complexity of Amazon phone screens and loop interviews.

Data modeling and ETL design interview questions

Atlassian analysts frequently work with large, event-based product datasets that require thoughtful schema design, reliable data pipelines, and mechanisms that prevent quality issues. These questions evaluate how you think about structure, scalability, and long-term maintainability.

1. How would you design a reporting data model for delivery times across cities and fulfillment centers?

   Begin by defining the correct grain, such as one row per shipment with timestamps for key delivery milestones. Add dimensions for geography, fulfillment center, carrier, and product characteristics. Address how you would support both daily aggregates and drill-down investigations through partitioning and well-defined keys.

   Tip: Always start with the business question and lock the grain. Most metric inconsistencies come from choosing the wrong level of detail.

2. Design a reliable ETL pipeline to ingest and transform Stripe transaction data.

   Outline ingestion from the API into storage, then detail transformations that standardize schema, timestamps, and currencies. Include idempotent loading, deduplication logic, and monitoring for missing or malformed files before writing into fact and dimension tables.

   Tip: Explain failure recovery. Atlassian values analysts who consider long-term data integrity, not just the ideal state.

3. How would you remove duplicate products listed under different sellers or names in a large ecommerce database?

   Propose a pipeline that uses text normalization, fuzzy matching algorithms, and metadata like SKU or model number to cluster similar listings. Outline a process for generating canonical product IDs and integrating human review for borderline cases.

   Tip: Emphasize a hybrid approach: automated matching plus manual checks. Scalability with accuracy matters.

4. How would you migrate a denormalized document database into a relational model for a social network?

   Identify core entities such as users, friendships, and interactions, then break embedded structures into normalized relational tables. Explain how you would reconcile duplicated or inconsistent attributes, choose primary keys, and design indexes that support common queries.

   Tip: Mention the need for a staged migration plan. Atlassian looks for analysts who consider operational risk, not just the final schema.

5. Suppose the marriage column is marked TRUE for all insurance customers. How would you debug this?

   Compare downstream data with raw source fields, inspect ETL scripts for default-value logic, and check audit logs for accidental overwrites. Investigate whether the issue arose from data entry errors, pipeline logic, or schema misconfiguration.

   Tip: Approach debugging systematically: isolate whether the error is upstream, in transformation, or in downstream reporting.

6. How would you match students to test records when the test table only has names and birthdates that may be inaccurate?

   Combine deterministic matching for exact cases with probabilistic or fuzzy matching for near matches. Assign a match score per candidate pairing, review low-confidence cases manually, and refine the matching rules iteratively.

   Tip: Highlight scalability. Build a matching pipeline that can be reused, not a one-off cleanup script.

   You can practice this exact problem on the Interview Query dashboard, shown below. The platform lets you write and test SQL queries, view accepted solutions, and compare your performance with thousands of other learners. Features like AI coaching, submission stats, and language breakdowns help you identify areas to improve and prepare more effectively for data interviews at scale.

Metric definition and insights interview questions

Atlassian analysts spend significant time defining metrics, diagnosing product changes, and translating ambiguous questions into measurable frameworks. These prompts assess how clearly you think about users, behaviors, and decision-making.

1. How would you define a north star metric for a new feature in a product like Jira or Confluence?

   Start by clarifying the feature’s user value: productivity, collaboration, or task completion. Propose a primary metric tied directly to this behavior, then layer guardrails such as churn, error rates, or customer satisfaction to prevent unintended optimization.

   Tip: Connect the metric to long-term outcomes, not surface-level activity. Atlassian values durable indicators of customer impact.

2. How would you analyze an A/B test that shows mixed signals across segments?

   Validate the experiment setup, then break results down by key segments like team size, geography, workspace type, or user tenure. Investigate confounders such as seasonality, traffic imbalance, or misaligned metrics, and propose next steps like segment-specific rollouts or iterative testing.

   Tip: Always summarize your interpretation in one clear sentence before diving deeper.

3. Design a KPI dashboard to track conversion funnels across multiple storefronts or product surfaces.

   Outline funnel stages such as impressions, clicks, activation events, and conversions, and segment them by platform, geography, or account type. Include trend lines, comparison views, and drill-down filters tailored for PMs, engineering, and design stakeholders.

   Tip: Describe who the dashboard is built for. A good KPI system is audience-specific.

4. How would you define and track delivery accuracy for operational or logistics-related workflows?

   Define accuracy as the percentage of items delivered within their promised window and segment results by geography or fulfillment process. Account for exceptions like rescheduled deliveries, partial orders, or multi-step workflows that can distort measurements.

   Tip: Discuss how you’d operationalize the metric, including alerting and reporting cadence.

5. How would you measure the impact of a new recommendation or ranking algorithm on user engagement?

   Compare a treatment group exposed to the new algorithm with a control group using metrics such as click-through rate, session depth, and long-term retention. Evaluate tradeoffs, including cases where short-term engagement rises but long-term satisfaction drops.

   Tip: Emphasize the importance of guardrail metrics to prevent harmful optimization.

6. How would you measure the success of Atlassian’s cloud migration journey for enterprise customers?

   Start by defining what “success” means for a migration: data completeness, time to completion, admin satisfaction, and post-migration usage recovery. Track activation and engagement patterns before and after migration, segmenting by workspace size, admin role, and product suite.

   Tip: Highlight the importance of stabilization metrics, such as “time to normal usage,” which are critical for enterprise-grade migrations.

7. How would you identify friction points in the Jira issue creation workflow?

   Map each step of the creation funnel (open → fill fields → assign → create) and compute drop-off rates at each step. Segment by role type, project configuration, and field complexity to determine which user groups or field types drive the highest friction.

   Tip: Propose actionable follow-ups, such as simplifying required fields or instrumenting new events. Atlassian values insights that lead to product changes.

Want more challenges? Test your skills with real-world analytics challenges from top companies on Interview Query. Great for sharpening your problem-solving before interviews. Start solving challenges →

Behavioral and values-based interview questions

Atlassian’s behavioral interviews focus on collaboration, clarity, user-centric thinking, and how you operate within a values-driven culture. These questions evaluate how you communicate, how you influence without authority, and how you use data to support teams and customers.

1. What are your strengths and weaknesses?

   This question assesses self-awareness and how you reflect on your working style. Atlassian values candidates who can openly acknowledge growth areas and demonstrate how they’ve adapted or improved their workflow.

   Tip: Tie strengths to behaviors that enhance team collaboration and tie weaknesses to honest, actionable improvements.

   Sample Answer: One of my strengths is structured communication, which helps cross-functional teams align quickly during ambiguous projects. A weakness I’ve worked on is over-polishing analyses; I now share early drafts sooner, which speeds up feedback cycles and leads to better outcomes.

2. Why do you want to work with us?

   Interviewers want to understand your motivations and whether you genuinely connect with Atlassian’s mission, products, and values. They look for answers rooted in the type of work analysts do, not generic interest in tech.

   Tip: Mention specific products, values, or collaboration principles that align with your working style.

   Sample Answer: I’m drawn to Atlassian because its products directly shape how teams collaborate, and I enjoy analytics work that influences user workflows. I also value the openness of Atlassian’s culture, which aligns with my approach to transparent communication and data-driven decision-making.

3. Talk about a time when you had trouble communicating with stakeholders. How did you overcome it?

   This question evaluates your ability to collaborate across roles with different technical backgrounds. Atlassian wants analysts who can translate complex insights into accessible narratives for PMs, engineers, and design partners.

   Tip: Show empathy and adaptability in how you tailored communication for different audiences.

   Sample Answer: I once worked with a non-technical stakeholder who struggled to understand a cohort model I built. I reframed the analysis using a simple visual timeline and focused on user behavior rather than statistical terminology. This improved alignment and led to productive follow-up discussions on product changes.

4. How comfortable are you presenting your insights?

   Analysts at Atlassian routinely share findings with cross-functional teams, so communication style matters as much as analytical depth. Interviewers want to gauge how confident and clear you are when presenting insights that drive product decisions.

   Tip: Highlight your ability to tailor the message to PMs, engineers, executives, and design teams.

   Sample Answer: I’m very comfortable presenting insights and often start by anchoring on the core question before walking through supporting evidence. In my previous role, I regularly briefed PMs and engineering leads on activation trends, which helped shape our roadmap for new user onboarding.

5. Tell me about a time you influenced a product or business decision using data.

   Atlassian looks for analysts who can turn insights into actionable decisions rather than just reporting numbers. Interviewers listen for how you framed the problem, how you partnered with PM or engineering, and what changed as a result.

   Tip: Highlight both the insight and the decision it enabled.

   Sample Answer: I analyzed a drop in feature engagement and discovered the decline was isolated to new users facing a hidden onboarding step. After sharing this with PM and design, we reworked the flow and saw a meaningful improvement in week-one activation.

6. Give an example of a time you worked through ambiguity and created structure for your team.

   Atlassian wants analysts who can bring clarity when requirements are incomplete or evolving. Your answer shows how you move a project forward even when definitions, signals, or resources are unsettled.

   Tip: Show how you created alignment without waiting for perfect information.

   Sample Answer: With unclear success metrics for a new collaboration feature, I drafted initial definitions and reviewed them with PM and engineering. This helped create shared alignment quickly and allowed us to begin measuring early adoption trends within the same week.

7. Tell me about a time you discovered a major data quality issue and how you handled it.

   Data quality is critical in a product-led company, so interviewers want to see how you diagnose issues, communicate them, and prevent recurrence. They also assess your sense of ownership.

   Tip: Include both short-term fixes and long-term preventative steps.

   Sample Answer: I noticed an unexpected spike in active users and traced it back to a pipeline change that duplicated events. I paused reporting, partnered with engineering to correct the logic, and added automated validation checks. This improved our QA process and prevented future anomalies.

In this video, Jay Feng, co‑founder of Interview Query and former data scientist at companies like Nextdoor and Monster, walks you through the top 5 insider interview questions that every data analyst should master before any interview. He explains how to think about these prompts, break them down clearly, and answer with depth and structure—covering product reasoning, analytics, metrics, and behavioral insight.

Watch it after reviewing the questions above to deepen your understanding of what interviewers really want and how to frame your responses in a way that demonstrates both technical skill and thought leadership.

Preparing for an Atlassian data analyst interview means combining strong technical fundamentals with clear communication, structured thinking, and alignment with Atlassian’s core values: open communication, balanced decision-making, deep customer focus, teamwork, and continuous improvement. Analysts are expected to not only analyze data but also guide product and design partners toward better decisions.

Below are the most effective strategies to prepare.

1. Practice structuring ambiguous product questions

   Atlassian analysts often work with open-ended prompts such as understanding why activation dropped or why team collaboration varies across products. Breaking down vague questions into clear definitions, hypotheses, and priority steps shows your ability to create structure where there is none.

   Tip: Before answering any ambiguous question, start by clarifying the objective, defining the terms, and outlining your investigation path.

2. Develop a working familiarity with Atlassian’s products

   Understanding how users create Jira issues, collaborate on Confluence pages, or plan work in Trello helps you reason more concretely about behavior and metrics. This familiarity also strengthens your answers in metric definition, experimentation, and insights.

   Tip: Create a sample Jira project or Confluence workspace and think about what events you would track and why they matter.

3. Be ready to justify every metric you define

   Atlassian values thoughtful decision-making, so interviewers look closely at why a metric exists, what it captures, and what guardrails accompany it. Your ability to explain the reasoning behind a metric matters more than choosing the perfect formula.

   Tip: When proposing any metric, include one risk or blind spot to show balanced, critical thinking.

4. Strengthen your communication for non-technical partners

   Analysts regularly work with PMs, engineering, design, research, and customer teams. The interview assesses how well you turn complex data into clear, actionable insights tailored to different audiences.

   Tip: Practice summarizing any complex analysis in two sentences: what happened and why it matters.

5. Show strong data quality instincts and integrity

   Atlassian’s products generate high-volume event data across many surfaces, which can lead to duplicates, partial events, timestamp gaps, or artifact noise. Showing that you naturally think about validation and consistency demonstrates ownership and customer focus.

   Tip: Make it a habit to mention quick validation steps like checking row counts, deduplicating events, and examining timestamp distributions.

6. Practice SQL patterns that reflect real product analytics

   Atlassian’s work involves funnels, cohorts, segmentation, active team metrics, workflow analysis, and multi-event journeys. SQL questions often require building logical, readable queries rather than clever one-liners. Scenario-based questions mirror Atlassian’s datasets well.

   Tip: Use tools like Interview Query’s SQL Scenario-Based Questions to practice realistic multi-step SQL logic that resembles Atlassian’s environment.

7. Prepare one portfolio analysis you can explain clearly

   Final rounds often ask candidates to walk through a past project. Choose one example that reflects how you think end to end, such as investigating a drop in activation or analyzing a feature’s impact.

   Tip: Structure your walkthrough as problem, approach, insight, and business implication, keeping it within 90 seconds.

8. Refine your STAR stories with an emphasis on teamwork and openness

   Behavioral interviews map directly to Atlassian’s values: openness, playing as a team, not harming the customer, and being the change you seek. Choose stories that reflect collaboration, thoughtful communication, and user-centered reasoning.

   Tip: End every STAR story with a measurable or observable outcome to reinforce effectiveness.

9. Simulate the interview environment with timed practice

   Practicing under realistic conditions helps you integrate SQL reasoning, product thinking, and communication flow. Mock sessions also help refine pacing and clarity.

   Tip: Use Interview Query’s mock interviews to practice analytics questions with structured feedback and improve your delivery.

How competitive is the Atlassian Data Analyst interview?

Atlassian’s process is selective because the role blends technical skill with product intuition and cross functional communication. Strong SQL, clear reasoning, and values-driven collaboration are all equally important. Candidates who can structure ambiguity and articulate insights clearly tend to stand out.

What is the typical timeline for the Atlassian interview process?

Most candidates move from recruiter screen to final decision within three to five weeks, depending on scheduling and team availability. Take-home tasks or technical assessments may add a few additional days. Communication from recruiters is generally consistent throughout the process.

Do I need advanced Python skills for this role?

Python is useful but not required. SQL is the primary tool for data analysts at Atlassian, especially for product analytics and funnel analysis. Light Python knowledge can help with ad hoc analysis or transforming datasets, but it is not the core focus of the interview.

What type of SQL questions does Atlassian ask?

Expect scenario-based SQL problems involving joins, window functions, funnel logic, event-level tables, and segmentation. These questions test real-world thinking rather than textbook problems. Practicing with multi-step SQL exercises that mimic product data is the best preparation.

How important are Atlassian’s core values in the interview?

Very important. Behavioral interviews are designed to assess openness, teamwork, customer focus, and continuous improvement. Interviewers look for collaborative thinking, transparent communication, and examples of owning problems or driving improvements.

Do Atlassian Data Analysts work closely with product teams?

Yes. Analysts typically partner with PMs, engineering leads, design, research, and customer-facing teams. Much of the role involves defining metrics, understanding user journeys, diagnosing product issues, and shaping decisions with evidence.

Is the data analyst role at Atlassian more product-focused or BI-focused?

It leans heavily product-focused. While some dashboarding and reporting is part of the work, the bulk of the role involves understanding user behavior, analyzing workflows, identifying friction points, evaluating experiments, and shaping product direction.

Does Atlassian support remote or hybrid work for analysts?

Many Atlassian teams are structured as remote-first or hybrid depending on location and business needs. Job postings typically specify whether a role is remote-friendly. Candidates should confirm location expectations with their recruiter early in the process.

Landing an Atlassian Data Analyst offer requires clarity, structure, and a deep understanding of how product teams make decisions. The most effective preparation blends scenario-based SQL practice, realistic mock interviews, and strong behavioral storytelling that reflects Atlassian’s values of openness, teamwork, and customer focus.

Interview Query gives you everything you need to prepare with intent. Build your skills using scenario-based SQL questions, simulate the real loop with mock interviews, and strengthen fundamentals with curated learning paths designed for data roles. Start practicing today and walk into your Atlassian interview with confidence.