# Analytics Engineer

**Company:** [Celonis](https://hotfix.jobs/companies/celonis)
**Location:** Madrid, Spain
**Role:** Data Engineering
**Experience:** 2+ years
**Skills:** SQL, Python, Statistical Modeling, Predictive Modeling, Data Modeling, Financial Modeling, Forecasting, Scenario Planning, Version Control, Data Visualization, Business Intelligence, Llm Assistants, AI Agents, Cursor
**Posted:** 2026-08-26

> Build data models, analyses, forecasts, automated dashboards, and AI-augmented analytics products for Finance and go-to-market operations. The role requires 2–4 years of analytical or analytics engineering experience, strong SQL and Python skills, and a quantitative degree.

## Job Description

## Responsibilities
- Partner with Finance and go-to-market operations stakeholders to translate business questions into structured analyses and recommendations.
- Build narratives, visualizations, KPIs, performance metrics, and analytical priorities.
- Develop and maintain data and financial models, forecasting logic, and scenario analyses.
- Apply statistical methods and predictive modeling to identify trends, risks, and opportunities.
- Build scalable automated dashboards and self-service data products.
- Write clean, reproducible, version-controlled SQL and Python code.
- Use LLM assistants, coding copilots, AI agents, and workflow automation to improve analytics workflows.
- Maintain data quality, consistency, and governance.
- Collaborate with Finance, adoption, GTM operations, and Data/Tech teams to embed data-driven practices in business processes.

## Requirements
- 2–4 years of experience in analytics engineering, data or business analytics, BI development, or a comparable technical and analytical role.
- Strong SQL skills and experience working with large, complex datasets.
- Hands-on Python experience for data analysis, automation, and statistical or predictive modeling.
- Foundations in data modeling or financial modeling, forecasting, and scenario planning.
- Working knowledge of statistical methods and rigorous analytical practices.
- Familiarity with version control, testing, documentation, and BI or data visualization tools.
- Ability to communicate insights clearly to non-technical stakeholders.
- Degree in Computer Science, Engineering, Mathematics, Statistics, Finance, Economics, or a related quantitative field.
- Exposure to modern AI and agentic tools, including LLM assistants, coding copilots such as Cursor, AI agents, and prompt-based automation.
- Exposure to Finance or GTM/revenue operations data.
- Ability to work effectively in a fast-paced, cross-functional environment.

## Compensation and Benefits
- Restricted Stock Units and merit-based refresh grants for full-time employees.
- Flexible hybrid work model.
- Paid parental leave, paid time off, learning and mentorship programs, Wellhub memberships, mental health counseling, wellness programs, and paid volunteer time.

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