EMEIA Sales Finance - Analytics Engineer

This role resides within the Process, Analytics, Reporting and Technology (PART) Enablement Team. We empower Apple’s EMEIA Sales Finance teams to operate more efficiently and make data-driven decisions by building and deploying innovative, user-friendly data applications. You will directly improve the day-to-day workflows of financial analysts, helping them unlock insights and drive business impact. This is a hands-on role where you’ll see your work used by key stakeholders across the organisation. We are looking for a strong Data Engineer with a passion for building intuitive web applications that solve real-world business problems. This role is at the intersection of data engineering, software development, and financial process optimisation. You will be responsible for designing, building, and deploying lightweight web interfaces and data tools – primarily using Python frameworks like Streamlit – to streamline financial analysis, reporting, and decision-making. You will also build data pipelines to support business needs. Success in this role requires an individual who can quickly understand complex financial processes, translate those processes into technical specifications, and deliver high-quality, user-friendly data applications. You’ll be a key collaborator with financial analysts, data scientists, and other engineers, building trust through clear communication and impactful solutions. Ideal candidates also have a strong understanding of UI/UX principles for designing intuitive, self-service data applications that drive user adoption across business teams with varying levels of technical expertise. Minimum Qualifications 5+ years of experience in data engineering or software development with a focus on building data applications. Strong proficiency in Python programming and experience with web frameworks (required: Streamlit, preferred: Flask, Django). Experience working with relational databases (e.g., SQL Server, PostgreSQL) and data lakes. Familiarity with data visualisation techniques and tools (e.g., Matplotlib, Seaborn, Plotly). Experience with version control systems (e.g., Git). Experience in modern data warehouses (e.g., Snowflake and Dremio) and ETL processes using modern orchestration tools (e.g. Airflow, Metaflow). Experience with cloud platforms (e.g., AWS, Azure, GCP) is a plus. Experience working with large-scale, complex financial or commercial datasets. Experience implementing data quality frameworks, monitoring solutions, and governance controls. Exposure to Dataiku or similar data science platforms. Experience working closely with analytics, BI, or data science teams to support downstream use cases. Strong problem-solving skills and attention to detail. Excellent communication and collaboration skills. Ability to communicate complex technical concepts clearly to non-technical stakeholders. Detail-oriented and self-motivated individual able to function effectively when working independently or in a team. Bachelor’s degree in Computer Science, Data Science, or a related field required. Preferred Qualifications Familiarity with UI/UX best practices, user-centred design, and data visualisation principles. Experience using Tableau or SAP BusinessObjects. Financial systems and/or Sales Finance domain experience is a plus.