Senior Data Engineer, DX
About The Position
DX is headquartered in Salt Lake City, Utah and is one of the fastest-growing SaaS companies globally. We help engineering leaders build high-performing, productive teams. DX collects millions of data points daily, powering insights into developer productivity and experience at companies like Pinterest, GitHub, BNY, Xero, and many more. Our business has scaled profitably and grown rapidly—tripling annual recurring revenue in the last several years. DX recently closed on its acquisition by Atlassian. By joining Atlassian, we will expand our resources, accelerate growth and R&D, and ultimately deliver greater impact to our customers.
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
Nice To Haves
- 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
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
Benefits
- health and wellbeing resources
- paid volunteer days
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
