Senior Data Engineer, DX

Atlassian, including its DX business, helps engineering leaders build high-performing, productive teams through software and data-driven insights. The Senior Data Engineer will own and maintain data pipelines, develop analytical data models, ensure data quality, support research and cross-functional data requests, and document data infrastructure for self-service use.

Responsibilities:

• Build and maintain data pipelines that extract, transform, and load live product data into research-ready formats (Postgres, data lake, or analytics warehouse)
• Design and optimize data models tailored to the recurring analyses behind our AI Impact Reports, DX Core 4 benchmarks, and industry-facing publications
• Collaborate closely with the research and engineering teams to understand analytical requirements and translate them into scalable, reproducible data infrastructure
• Ensure data quality and consistency: you care about definitions, edge cases, and making sure the same question gets the same answer every time
• Support ad hoc data pulls for time-sensitive research and cross-functional requests from Sales, Customer Success, and product teams
• Document everything - schemas, transformation logic, data dictionaries, and pipeline dependencies so that analysts and researchers can self-serve confidently

Requirements:

• • Strong SQL skills: you can write complex queries, optimize performance, and model data for analytical workloads
• • Hands-on experience with Postgres or similar relational databases, including working with semi-structured data (JSONB, nested fields)
• • Experience building and maintaining ETL/ELT pipelines that move data from production systems into analytics-ready formats
• • Comfort working with large, messy, real-world datasets: you know how to clean, normalize, and validate data at scale
• • Strong documentation habits: you write clear schema docs, data dictionaries, and pipeline runbooks without being asked
• • Self-motivated and reliable: you can manage recurring deadlines (e.g., quarterly reporting cycles) with minimal oversight
• • Experience working with SaaS product data, telemetry, or event-driven data
• • Familiarity with developer tooling data: Git/GitHub/GitLab/Bitbucket activity, CI/CD pipeline metrics, Jira issue data
• • Exposure to survey data, time-series analysis, or benchmarking methodologies