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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Analytics Engineer - Enpal Energy - **Company:** Enpal GmbH - **Location:** Berlin, Germany - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Query Performance, Adaptable Database Systems, Artificial Intelligence, Amazon Web Services, Data Analysis, Business Logic, Microsoft Azure, Big Data, Continuous Integration, Python (Programming Language), Meta-Data Management, Performance Tuning, SQL Databases, Tableau (Software), Snowflake, Git, Data Analytics, Software Version Control, Data Pipelines - **Published:** August 6, 2026 - **Apply:** https://de.indeed.com/viewjob?jk=a6ea7b083bbf2ac1 ## About the Role * 3+ years of experience as an Analytics Engineer, BI Developer, or Data Engineer. * Proven record of turning complex, large-scale data into actionable insights and governed metrics used across BI. * Expert in SQL and data modeling (star schemas, dimensions/facts, semantic layer design). * Hands-on experience building production-grade dbt projects with testing, documentation, and version control workflows. * Strong proficiency in Python for analytics tasks (data checks, automation, utilities). * Strong stakeholder management and communication skills - able to translate business logic into durable, scalable data models. * Familiarity with ELT concepts, CI/CD for analytics code, data cataloging, and basic cost/performance tuning. * Self-starter with a hands-on, problem-solving mindset, comfortable balancing autonomy and collaboration in a fast-moving environment. * Genuine curiosity about how AI is reshaping the analytics engineering role - comfortable experimenting with AI-assisted development tools and motivated to help the team adopt them thoughtfully. * Experience with or strong interest in semantic layer tooling (dbt MetricFlow, Snowflake semantic views, or similar) and how it enables both self-service analytics and AI-ready data products. ## Description * Model clean, well-documented datasets in dbt (staging marts), including tests, ownership, and clear metric definitions. * Lead the development and evolution of our semantic layer - defining governed business metrics, dimensions, and data contracts that serve as the single source of truth to downstream AI tools. * Structure dbt models, metric definitions, and documentation so they are machine-readable and ready to serve as reliable context for AI-powered analytics tools. * Diagnose messy or ambiguous source data, reconcile inconsistencies, and align stakeholders on a single source of truth. * Optimize Snowflake query performance, data freshness, and quality through testing and monitoring. * Partner with BI and business teams to design certified datasets, dashboards, and self-service standards. * Develop reporting end-to-end - from understanding business needs to delivering actionable, intuitive data products. * Maintain and evolve central data pipelines, dbt models, and Tableau integrations within a modern stack (Python, AWS/Azure/GCP, Git). * Act as a technical mentor for the team, helping others adopt best practices in analytics engineering and embrace new tooling - including AI-assisted development. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Making Data Warehouses fast. 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