Principal Sales Infrastructure Analyst - Analytics Engineering

Atlassian develops software that helps teams collaborate and work more effectively. The Principal Sales Infrastructure Analyst will align enterprise go-to-market and finance data by designing robust pipelines, monitoring data consistency across business systems, and developing scalable data products and AI context layers. The role also partners cross-functionally to improve data quality, support analytics engineering practices, and reduce manual processes.

Responsibilities:

• 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 requirements → UAT → DE implementation 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

Requirements:

• 6+ years of experience in Analytics Engineering, Data Engineering, Data Science, Business Intelligence, or similar field
• Expert level proficiency with SQL and data modeling. You know how to architect data models and influence engineering teams to build for scale and consistency
• Proficiency in a quantitative programming language, preferably Python
• Experience with BI tools such as Tableau, Looker, and know how to enable self-service analytics
• Experience with CRM systems like Salesforce or Hubspot and ERP systems like Oracle or Netsuite
• Understand effective software and analytics engineering practices and can use AI tools to move faster without creating brittle systems, unclear code, or unnecessary complexity
• Ability to independently structure complex, ambiguous problems from initial framing through delivery and execution with diverse teams up to the senior leadership level
• Comfortable collaborating across numerous horizontal partners (sales systems, sales enablement, sales compensation) as well as departmental/vertical stakeholders (marketing, product, finance) to build alignment and deliver results
• Excellent problem-solving skills with a focus on delivering business impact through reliable systems