Job description

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 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 • 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 • Proactive engagement with the management team to identify, prioritize, develop, and execute strategies that improve our data to enable better decisionmaking • 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 Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes. 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 go.atlassian.com/payzones 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.

Requirements

Must-haves

Working at Atlassian
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
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
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
Proactive engagement with the management team to identify, prioritize, develop, and execute strategies that improve our data to enable better decisionmaking
Excellent problem-solving skills with a focus on delivering business impact through reliable systems

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

goaiaccessibilitysqlpython

Platform: atlassian
Effort: medium