Senior Data Engineer, DX

Analytics & Data Science | Salt Lake City, United States | Remote, Remote |

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.

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, Git Hub, 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.

What we value at DX

Companies have all kinds of culture slides. At DX, we want to be very clear about what we care about and how we judge performance. For us, it all boils down to individual mastery, becoming the best at your craft. Those who exhibit this quality will thrive here and be unduly rewarded. We can’t control outcomes due to competitors, the economy, decision-makers, etc.,

but what we can control is doing our jobs at the highest level possible

What you’ll do

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

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.

Please visit  for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

This role may also be eligible for benefits, bonuses, commissions, and equity.

In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:

Zone A: $167,400 - $218,550

Zone B: $150,660 - $196,695

Zone C: $138,942 - $181,397

What we’re looking for

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 have:

Experience working with SaaS product data, telemetry, or event-driven data

Familiarity with developer tooling data:
Git/Git Hub/Git Lab/Bitbucket activity, CI/CD pipeline metrics, Jira issue data

Exposure to survey data, time-series analysis, or benchmarking methodologies

Why this role

High visibility:
The data you shape will underpin…