1. What is a Data Engineer?

A Data Engineer at Atlassian builds and operates the data platforms that power insights across products like Jira, Confluence, Bitbucket, and Trello. Your pipelines and models enable product analytics, growth experimentation, billing and finance reporting, reliability engineering, and customer trust and safety. The scale is global and multi-tenant, with diverse workloads spanning batch and streaming—expect challenges around data quality, lineage, cost, and privacy.

You will turn raw product and platform exhaust into reliable, well-modeled datasets that analysts, data scientists, and product teams use daily. That means production-grade SQL, robust Python data transformations, dimensional modeling that stands up to evolving business logic, and pragmatic architectural choices across cloud data warehouses and distributed processing engines.

This role is both technical and product-adjacent. You will partner with product managers, analytics leaders, and software engineers to define telemetry, model business concepts (accounts, subscriptions, active users, funnels), and ship high-signal datasets with SLAs. Strong engineers here obsess over correctness, simplicity, and maintainability, not only throughput.

2. Getting Ready for Your Interviews

Approach preparation in layers: first master the fundamentals (SQL accuracy and Python correctness), then consolidate data modeling and warehousing concepts, and finish with systems thinking for pipeline and big data design. Expect interviews that prioritize hands-on SQL and Python first, followed by deeper probes into modeling, architecture, and values alignment.

Role-related knowledge – Atlassian’s DE interviews are SQL- and Python-forward. You will be evaluated on correctness, performance awareness, and code readability. Demonstrate mastery of joins, window functions, partitioning, schemas, dbt-like ELT patterns, and Spark/Kafka familiarity.

Problem-solving ability – Interviewers look for how you decompose ambiguous problems, validate assumptions, and iterate. Frame constraints (latency, cost, SLAs), justify trade-offs, and show how you fail fast while preserving data quality.

Execution rigor – This measures your ability to write production-grade code and queries. Expect to add tests, cover edge cases, explain how you’d monitor jobs, and design for idempotency and backfills.

Collaboration and communication – You will need crisp explanations for non-technical partners and tight coordination with SDEs and analysts. Use precise language, narrate your thought process, ask clarifying questions, and reflect Atlassian’s collaborative style.

Values alignment – Atlassian cares about customer impact, openness, and teamwork. Show how you make decisions that protect customer trust, how you document for others, and how you handle conflict and feedback.

3. Interview Process Overview

From recent 1point3acres reports, you should expect an initial recruiter conversation, a coding screen that is primarily SQL and Python, and then a technical deep dive that continues those themes and may add data warehousing/modeling and architecture. Some candidates complete an online assessment (e.g., Hackerrank) with multiple SQL questions, while others face a live environment with a blend of SQL and Python under time pressure. A values or “resume walk-through” conversation typically appears at the end.

The process is efficient and feedback-oriented. Several candidates received scheduling within 1–2 weeks and results within days. Interviews emphasize practical, job-relevant tasks: writing multi-step SQL transformations, implementing Python data manipulation, and discussing dimensional modeling and big data design. Compared to some companies, Atlassian’s DE interviews are less about abstract algorithms and more about building real pipelines with clear business context.

This timeline visual highlights the typical flow: recruiter alignment, a SQL/Python screen, a technical deep dive (modeling/architecture), and a values round. Use it to plan energy: front-load SQL/Python drills before the screen, then shift to modeling and system design. Expect variations by location and team; senior candidates may see deeper architecture discussions.

4. Deep Dive into Evaluation Areas

SQL for Analytics and Pipelines

SQL is the backbone of the process. You will write multi-step transformations, often with chained questions where each step feeds the next. Strong performance looks like correct answers first, then clear structure, edge case handling, and an ability to reason about performance (indexes, partitions, window function costs).

Be ready to go over:

• Joins and filtering correctness – Inner vs. left joins, semi/anti joins, deduplication patterns, null semantics.
• Window functions – Ranking, partitioned aggregates, gaps-and-islands, sessionization.
• Time-series and incremental logic – Slowly changing patterns, late-arriving data, watermarking, validity intervals.

Advanced concepts (less common):

• Performance tuning and exploitation of partitions/clustering
• MERGE/UPSERT semantics and idempotent backfills
• Data quality constraints and anomaly detection in SQL

Example questions or scenarios:

• “Given events(user_id, event_time, event_type), compute daily active users and a 7-day rolling retention metric.”
• “You have orders and refunds. Produce net revenue by week with late refunds applied correctly.”
• “Three-step task: build a sessions table, compute per-session conversion, then join to user attributes for a cohort analysis.”

Python for Data Engineering

Expect Python tasks that manipulate records, parse logs, or implement transformation logic you might otherwise write in SQL. Strong solutions demonstrate clean structure (functions), testable logic, and linear-time reasoning with attention to memory.

Be ready to go over:

• Parsing and transformation – Reading line-oriented logs/JSON, normalizing fields, handling malformed records.
• Aggregation and grouping – Rolling and windowed computations, map-reduce style grouping.
• Data validation – Asserting schema, filtering bad data, simple unit checks.

Advanced concepts (less common):

• Iterators and generators for streaming
• Pandas vs. pure-Python trade-offs in production
• Type hints, docstrings, and basic test scaffolding

Example questions or scenarios:

• “Parse web server logs and compute the top N endpoints per user in the last 24 hours.”
• “Given a list of events with timestamps, deduplicate by key keeping the most recent, then output time-bucketed counts.”
• “Transform nested JSON payloads into a normalized structure suitable for warehousing.”

Data Warehousing and Dimensional Modeling

Round 2 for many candidates explores modeling. Interviewers assess if you can translate business processes into resilient schemas and explain choices. Strong answers show business understanding, naming rigor, and a plan for change management.

Be ready to go over:

• Star vs. snowflake – When to denormalize for analytics and performance.
• Slowly Changing Dimensions (SCD) – Type 1 vs. Type 2 trade-offs and how to implement them.
• Grain definition – Choosing the right grain for facts, surrogate keys, and surrogate vs. natural keys.

Advanced concepts (less common):

• Bridge tables for many-to-many
• Data privacy and PII handling in models
• Data vault or ELT layering with dbt-like patterns

Example questions or scenarios:

• “Model product usage for Jira issues, users, and projects to support daily active metrics and retention.”
• “Design a subscription and invoicing model handling upgrades, proration, and refunds.”
• “Explain how you would implement SCD Type 2 for account attributes and query ‘as-of’ states.”

Big Data and Distributed Processing

Some interviews include architecture or Spark/Kafka topics. You will be evaluated on knowing when to use distributed systems, how to partition data, and how to make pipelines resilient and cost-aware.

Be ready to go over:

• Spark fundamentals – Wide vs. narrow transformations, shuffles, joins, and checkpointing.
• Streaming vs. batch – Latency vs. correctness trade-offs, exactly-once semantics.
• Storage and file layout – Parquet/ORC, partitioning strategies, small files problem.

Advanced concepts (less common):

• Stateful streaming with watermarks
• Skew handling (salting, broadcast joins)
• Schema evolution and compatibility

Example questions or scenarios:

• “Design a pipeline to process clickstream events in real time for feature flags exposure and conversions.”
• “Given skewed keys in Spark, how would you optimize a join to avoid OOM errors?”
• “Outline a backfill strategy for a historical table with late-arriving events.”

Pipeline and Platform Design

You may be asked to design an end-to-end architecture for a specific Atlassian-style analytics use case. Strong performance includes clear component boundaries, orchestration, observability, and failure recovery.

Be ready to go over:

• Orchestration and lineage – Airflow-style DAGs, retries, idempotency, data contracts.
• Monitoring – SLAs, SLOs, data quality checks, alerting thresholds.
• Cost and reliability – Storage tiering, cluster autoscaling, partition pruning.

Advanced concepts (less common):

• Data mesh patterns for domain ownership
• Row vs. columnar store trade-offs for workloads
• Multi-region replication and disaster recovery

Example questions or scenarios:

• “Propose a data platform to support A/B testing metrics with trustworthy guardrails and reproducibility.”
• “How would you implement data quality checks that block downstream jobs on critical failures?”
• “Design a CDC-based pipeline from OLTP to a cloud data warehouse with late-arrival handling.”

Values, Collaboration, and Delivery

Atlassian values open communication, customer-centric decisions, and teamwork. Interviewers evaluate how you document, review, and iterate with peers, and how you keep customer trust top of mind.

Be ready to go over:

• Stakeholder alignment – Translating ambiguous asks into specific metrics and tables.
• Documentation and reviews – RFCs, ADRs, and PR etiquette.
• Incident response – Communicating outages, root cause analysis, and prevention.

Advanced concepts (less common):

• Balancing speed vs. quality under deadlines
• Navigating trade-offs with product and infra constraints
• Mentoring and uplifting team standards

Example questions or scenarios:

• “Tell us about a time you protected data quality under pressure.”
• “Describe a difficult stakeholder request and how you clarified scope and success metrics.”
• “How do you handle a breaking change your pipeline caused downstream?”

This word cloud highlights high-frequency topics—expect prominence for SQL, Python, modeling, and Spark/architecture. Use it to prioritize: master SQL first, then Python transformations, then modeling, and finally big data/architecture for differentiation. Lower-frequency items can be quick refreshers unless the role/level signals deeper coverage.

5. Key Responsibilities

As a Data Engineer at Atlassian, you own the lifecycle of analytical and operational data pipelines. You partner closely with analytics, product, finance, and reliability teams to surface trustworthy data for decisions and automation. You’ll design warehouse schemas, implement batch and streaming jobs, and establish observability and governance so teams can ship confidently.

You will translate product telemetry and platform logs into curated datasets with clear contracts. That includes defining grains and keys, implementing SCD, and setting SLAs for freshness and completeness. On the platform side, you’ll standardize ingestion patterns, optimize storage formats and partitioning, and guide cost-efficient compute practices.

Collaboration is routine: you’ll work with SDEs to instrument events, with analysts to finalize business logic, and with security/privacy partners to enforce PII handling. Typical initiatives include building product usage marts for Jira/Confluence, standardizing experimentation metrics, improving billing and revenue data pipelines, and deploying QA checks to reduce breakages.

• Build and maintain ELT/ETL pipelines across batch and streaming.
• Design dimensional models and data marts for high-usage domains.
• Implement data quality checks, lineage, and documentation.
• Optimize jobs for performance, cost, and reliability.
• Participate in code reviews, incident response, and continuous improvement.

6. Role Requirements & Qualifications

A strong candidate demonstrates deep SQL and practical Python, fluency in modeling, and working knowledge of modern cloud data stacks. Experience shipping production pipelines, supporting stakeholders, and maintaining data quality is essential.

• Must-have skills

  • Strong, production-grade SQL (window functions, complex joins, incremental loads).
  • Solid Python for data transformations and tooling.
  • Data modeling (star schemas, SCDs, grain definition).
  • Experience with a cloud data warehouse (e.g., Snowflake, Redshift, BigQuery) and orchestration (e.g., Airflow).
  • Familiarity with distributed processing (e.g., Spark) and streaming concepts (e.g., Kafka/Kinesis).
  • Data quality, testing, monitoring, and documentation practices.
  • Version control and CI/CD fundamentals.

• Nice-to-have skills

  • dbt or similar ELT framework; schema registry and data contracts.
  • Scala for Spark, or advanced PySpark optimization.
  • Observability stacks for data (e.g., Great Expectations, Monte Carlo).
  • Experimentation/metrics platform experience.
  • Infrastructure-as-code and cost optimization in cloud environments.

• Experience level

  • Roles span early-career to senior. Several reports cite openings targeting 3–4 years of experience, with senior roles emphasizing architecture depth and cross-team leadership.

7. Common Interview Questions

These questions are representative of recent 1point3acres reports and may vary by team. Use them to recognize patterns and rehearse your approach; do not memorize exact answers.

SQL

Tests correctness, compositional thinking, and ability to chain transformations.

• Write a query to compute 7-day rolling retention for active users by product.
• From events with user_id, timestamp, event, build sessions (30-minute timeout) and compute conversion per session.
• Given orders and refunds tables, calculate net revenue per week with late refunds reconciled.
• Identify the top N projects per month by active issues, breaking ties deterministically.
• Implement an idempotent monthly incremental load with MERGE semantics.

Python

Evaluates clean data transformation logic and attention to edge cases.

• Parse JSON logs with potential missing fields; output normalized rows and a reject list.
• Deduplicate events by (user_id, type) keeping the latest timestamp efficiently.
• Implement sliding window counts over timestamped events without using external libs.
• Given large CSV chunks, compute funnel drop-off by stage in streaming fashion.
• Write tests for your function to validate corner cases.

Data Warehousing and Modeling

Assesses your ability to translate business processes into schemas.

• Design a data model for Jira issues, sprints, and users to support velocity and throughput analytics.
• Explain when you’d use SCD Type 1 vs. Type 2 for account attributes; show sample SQL.
• Choose star vs. snowflake for a subscriptions domain with product and pricing hierarchies.
• Propose surrogate key strategy for joining cross-product user identities.
• How would you handle late-arriving facts in your model and keep queries accurate “as of” a date?

Big Data and Architecture

Focuses on system design, scalability, and reliability.

• Design a real-time pipeline for feature flag exposure and conversion with exactly-once semantics.
• Optimize a skewed Spark join between a large and a small table; discuss broadcast and salting.
• Propose a backfill strategy for a two-year historical dataset without disrupting SLAs.
• How would you manage schema evolution in Parquet with downstream compatibility?
• Outline monitoring and alerting for data freshness, completeness, and distribution drift.

Behavioral and Values

Explores collaboration, openness, and customer impact.

• Tell us about a time you prevented a data quality issue from reaching customers.
• Describe a disagreement about modeling with analysts and how you resolved it.
• How do you communicate a pipeline incident and drive a no-blame postmortem?
• Example of making a cost-quality trade-off and how you justified it.
• How do you ensure your work is discoverable and reusable by other teams?

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

8. Frequently Asked Questions

Q: How difficult is the interview and how long should I prepare?
Expect medium rigor focused on practical SQL and Python. Two to four weeks of targeted practice on production-style SQL and Python, plus a week on modeling and architecture, is typical for strong performance.

Q: What differentiates successful candidates?
Clear, correct SQL under time pressure; clean, testable Python; crisp modeling decisions with rationale; and structured architectural trade-offs. Strong candidates narrate assumptions, validate edge cases, and connect decisions to customer impact.

Q: What is the typical timeline from screen to decision?
Reports suggest a fast cadence: scheduling within 1–2 weeks, and feedback often within days after each step. Timelines vary by location and team load.

Q: Will there be a big data or architecture round for non-senior roles?
Some teams include a light architecture/design segment even for mid-level roles. Depth scales with seniority; prepare fundamentals (Spark basics, streaming vs. batch, partitioning).

Q: Is the process remote-friendly?
Yes—many interviews run online. Plan for stable connectivity and practice in browser-based coding environments.

Q: How values-focused is the final round?
Expect a straightforward values/resume round. Prepare concrete stories demonstrating customer focus, openness, and teamwork.

9. Other General Tips

• Prioritize SQL accuracy over premature optimization: Solve the problem end-to-end, validate with small samples, then discuss performance improvements.
• Narrate your approach: State assumptions, data shapes, and edge cases out loud; interviewers reward structured thinking and collaboration.
• Model with the business in mind: Start with the grain. Name facts and dimensions with clarity and document SCD choices and “as-of” semantics.
• Design for reliability: In architecture answers, explicitly cover idempotency, retries, backfills, and data quality checks.

• Demonstrate cost awareness: Mention partition pruning, storage formats, and job scheduling that minimize spend while meeting SLAs.
• Connect to Atlassian’s values: Show how your decisions protect customer trust and enable teammates via documentation and reusable patterns.

10. Summary & Next Steps

The Data Engineer role at Atlassian blends practical engineering with product impact at global scale. You will shape the datasets that drive decisions across Jira, Confluence, and more—work that directly affects customers and teams. The interview process mirrors the job: hands-on SQL, pragmatic Python, solid modeling, and thoughtful architecture.

Center your preparation on four pillars: SQL correctness, Python transformations, dimensional modeling, and pipeline design fundamentals. Expect chained SQL questions, one or more Python prompts, and discussions on SCDs, grain, Spark basics, and end-to-end architecture. Values conversations reward clear communication, openness, and customer-first thinking.

Focused, high-fidelity practice materially improves outcomes. Build muscle memory on multi-step SQL, write clean Python with tests, and rehearse modeling and architecture explanations with trade-offs. Explore more insights and resources on Dataford to round out your prep. You have the skills—now structure your preparation to showcase them with confidence.

This module outlines typical compensation components (base, equity, bonus) and ranges by level and location. Use it to calibrate expectations and prepare data-driven questions for recruiters. Remember that offers vary by seniority and market; leverage leveling information and your experience scope when negotiating.