> Markdown version of [/jobs/ext/3678942-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/3678942-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** Statista GmbH - **Location:** Berlin, Germany - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, BigQuery, Cloud Database, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Transformation, Data Structures, Data Warehousing, Database Queries, Dimensional Modeling, Document-Oriented Databases, Claude Code, Snowflake, Amazon Redshift - **Published:** October 11, 2026 - **Apply:** https://www.adzuna.de/details/5921020687 ## About the Role * 3+ years of experience in analytics engineering, data engineering, or a related role * Strong SQL skills and solid experience in data modeling (e.g., dimensional modeling, star schemas) * Hands-on experience with modern data stack tools and technologies (e.g., dbt, CI/CD) and openness to AI-assisted development workflows (e.g., Claude Code or similar) * Experience working with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) * Strong attention to detail with a quality-focused and structured working style * Interest in data governance, standards, and scalable data architecture * Analytical mindset with strong problem-solving skills * Excellent communication skills in English (German is a plus), with both technical and business stakeholders, combined with a solid understanding of business requirements and contexts ## Description * Design, build, and optimize scalable data models using modern data stack tools (e.g., dbt, Snowflake) * Transform raw data into clean, reliable, and well-documented datasets for analytics, reporting, and operational use * Collaborate closely with data engineers, analysts, tracking teams, and business stakeholders to define data requirements and ensure alignment * Implement and maintain data quality checks, testing, and monitoring to ensure accuracy and reliability * Develop and manage semantic layers to standardize and govern key business metrics * Improve data accessibility and usability across teams by promoting best practices and clear data structures * Document data models, definitions, and workflows to enhance transparency and data literacy * Contribute to data governance, harmonization, and automation initiatives, including enabling AI-driven analytics use cases