QUALIFICATIONS AND EXPERIENCE

• Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related field.
• Extensive experience in data analytics and visualization, with a proven track record of delivering insights that drive business value.
• Expertise in data modeling, including the ability to build and manage semantic models via code (e.g., version control, automated testing, script-driven deployment).
• Strong coding proficiency in Python and SQL, with demonstrated ability to manage codebases using Git and CI/CD workflows.
• Strong data storytelling skills, using visualization platforms such as Streamlit, Power BI, or Tableau.
• Effective communication and stakeholder management skills, adept at translating complex technical details into clear business outcomes.
• Experience working in Agile/Scrum environments, including sprint planning and iterative development.
• Familiarity with cloud platforms (e.g., AWS) and data warehousing (Snowflake)
• Baseline knowledge of ML and data engineering practices beyond core analytics (nice to have)
  If you’re excited to elevate Fortescue’s analytics capabilities—bridging the gap between raw data and meaningful insights—and you thrive in a fast-paced, code-centric environment, we’d love to hear from you. Apply now to join us in shaping the future of data and AI at Fortescue!

ABOUT US

Be part of something big. Fortescue is leading the world with our plan to decarbonise our iron ore operations, projects that harness renewable energy and the development of technology that will change our planet forever.

• Champion analytics development & best practices
• Employ Git-based workflows to manage semantic models, ensuring robust version control and rapid iteration.
• Coach team members on efficient, code-driven approaches to data manipulation and analysis.
• Design & optimize analytics workflows
• Transform raw data into curated datasets on cloud platforms like AWS and Snowflake, ensuring reliability and scalability without needing to build full enterprise pipelines.
• Implement agile data wrangling processes with Python (e.g., Pandas, NumPy) to deliver high-quality, timely insights.
• Create compelling data narratives
• Utilize Streamlit, Power BI, and other visualization tools to produce clear, impactful dashboards and reports.
• Translate complex findings into actionable insights for stakeholders, fostering data-driven decision-making.
• Collaborate & mentor
• Work within Agile/Scrum teams, contributing to project planning and sprint reviews.
• Guide analytics developers across the business, facilitating skill development in analytics, coding, and best practices.
• Stay at the forefront of innovation
• Keep an eye on emerging analytics technologies and methods (e.g., advanced Python libraries, new BI tools).
• When needed, tap into baseline ML or data engineering knowledge to expand project capabilities—though your core focus is analytics.
• Foster stakeholder engagement
• Partner with business users to gather requirements, understand pain points, and refine analytics outputs for maximum impact.
• Clearly communicate progress and findings to both technical and non-technical audiences.