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Celonis is seeking an Analytics Engineer to join the Data & Transformation team within the Strategic Intelligence & Business Partner pillar. This is a hands-on technical role embedded close to the business, responsible for elevating data-driven decision-making across Finance and go-to-market functions.
You will build models, analyses, and automated data products that support smarter decisions and better performance across Finance, adoption, and GTM operations. Key responsibilities include:
**Analytics & Business Support**: Partner with stakeholders across Finance and GTM operations to translate business questions into structured analyses and clear recommendations. Help build narratives and visuals that make complex data understandable, and contribute to quarterly analytics priorities.
**Advanced Analytics & Modeling**: Build and maintain robust data and financial models, forecasting logic, and scenario analyses. Develop and refine KPIs and performance metrics that give visibility into business drivers. Apply statistical methods and predictive modeling to anticipate trends and surface risks and opportunities early.
**Data Products, Automation & AI**: Build scalable, automated dashboards and data products that improve the speed and accuracy of insight. Write clean, reproducible, version-controlled code in SQL and Python. Leverage modern AI and agentic tooling—LLM-based assistants, coding copilots (e.g., Cursor), AI agents, and workflow automation—to accelerate work and reduce manual effort. Maintain data quality, consistency, and governance across datasets.
**Cross-functional Collaboration**: Work closely with Finance, adoption, GTM operations, and Data/Tech teams to identify analytics opportunities and embed data-driven thinking into core business processes.
**Required Qualifications**: 2–4 years of experience in analytics engineering, data/business analytics, BI development, or comparable technical and analytical role. Strong SQL skills and confidence working with large, complex datasets. Hands-on experience with Python for data analysis, automation, and statistical/predictive modeling. Solid foundations in data modeling, financial modeling, forecasting, and scenario planning. Working knowledge of statistical methods and analytical mindset grounded in sound methodology. Familiarity with software engineering best practices (version control, testing, documentation) and BI/data visualization tools. Ability to translate data into clear insights and communicate effectively to non-technical stakeholders. Comfortable in fast-paced, cross-functional environments. Degree in Computer Science, Engineering, Mathematics, Statistics, Finance, Economics, or related quantitative field. Exposure to modern AI and agentic tools (LLM assistants, coding copilots, AI agents, prompt-based automation) and interest in applying them.
