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Atlassian

Principal Sales Infrastructure Analyst, Analytics Engineering

Reposted 8 Hours Ago
In-Office or Remote
Hiring Remotely in Chile
Senior level
In-Office or Remote
Hiring Remotely in Chile
Senior level
Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.
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Analytics & Data Science | Remote, Americas | Remote, Remote | Austin, United States | Salt Lake City, United States
Working at Atlassian
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.
We're looking for a versatile, seasoned sales data analyst or analytics engineer that will focus on driving data alignment (improving business processes and production systems) between enterprise go-to-market and finance. You'll design clean, robust data pipelines and infrastructure to monitor data drift between systems/applications like Salesforce, Oracle Fusion, Gainsight, and Databricks. As an experienced senior analytics engineer on our team, you'll help evolve our data stack, champion best practices in data processing + analytics design, and build a robust context layer on top of our data that enables AI agents to interact safely.
  • Design, build, and maintain data infrastructure that ensures data quality, consistency, and accessibility for analytical workflows
  • Lead the development of data products and an AI context layer that enables analytics and data access to scale across the company
  • Build and maintain canonical datasets by developing high-volume, reliable ETL/ELT pipelines using data lake and data warehousing concepts
  • Partner with finance, data engineering, and other x-functional teams to track and flag data inconsistencies that obscure the company's ability to accurately close our books
  • Execute sizing analyses for transformational and ad-hoc data infrastructure changes, including well-defined impact analyses to support new and ongoing initiatives and product launches, conducting investigations and QA on source data quality
  • Develop SME-level knowledge and ownership for core system data transformations (owning the cycle for a new product or data structure)
  • Leverage available tools to find efficiencies and process improvements to help us and our partner teams (deal desk, central analytics) remove manual processes and improve source data accuracy
  • Investigate reported bugs or deficiencies, working with business analysts and data engineering to do UAT/QA on simple bug-fix & feature releases

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.
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