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Siemens is seeking a senior data engineer to own end-to-end data models for sustainability and regulatory reporting. You will build Snowflake models with dbt, ensure data quality, and work directly with Sustainability teams across Europe to translate requirements into data products.
The role requires 5+ years in data engineering, expert SQL, Snowflake, and Python, with experience in Git-based CI/CD and agile DevOps. English fluency is essential. Hybrid work with a home office budget is offered.
The AI & Data Enablement team builds the data and AI foundation that lets DI's business functions work smarter - and for Sustainability, that means the data behind Siemens' environmental, regulatory and internal sustainability reporting. Your mission will be… We're looking for a senior data engineer who owns data models end to end - from a business question to a tested, documented, productive solution. This is neither a pure analyst role nor a ticket-processing developer role. You will build and run the Snowflake data models for sustainability and compliance topics such as life cycle assessments, regulatory reporting and internal sustainability KPIs, and you will work directly with the Sustainability business teams in Germany to turn their requirements into data products that are actually
Design, build and optimise data models in Snowflake for sustainability and compliance topics - from staging to marts - with dbt, including tests and documentation. Deliver to production via GitLab and dbt Cloud: merge requests, reviews, scheduled jobs, deployments. Own data quality: tests on uniqueness, grain and references, reconciliation against source systems, root-cause analysis of mismatches. Onboard new sources and reference data into the existing dbt project, including incremental logic and performance tuning on large BOM-level tables. Work with business users to collect, structure and refine requirements and translate them into clear, testable acceptance criteria. Answer ad-hoc data questions and support reporting consumers (e.g. Power BI). Contribute to the continuous improvement of our data engineering standards, processes and solution adoption in sustainability projects. Use modern AI-assisted developer tooling (e.g. LLM coding assistants) to work faster and better.
5+ years of experience in data engineering or analytics engineering, delivering data products to production. Expert SQL and data modelling; proven hands-on expertise in Snowflake. Solid experience modelling data with dbt (dbt Core and/or dbt Cloud), including tests and documentation. Confident with Git-based workflows and CI/CD basics (GitLab or GitHub). Programming skills in Python. Experience in agile / DevOps set-ups. Degree in Computer Science, Information Systems, (Industrial) Engineering, Environmental Management or a related field - or equivalent experience. Fluent written and spoken English. Model data cleanly and explain why. Take a requirement from a business conversation to a productive, tested data product. Communicate clearly with technical and non-technical stakeholders. Balance speed with quality, documentation and maintainability. Take ownership of your models from concept to production. Are curious about the business meaning behind the data - environmental footprint, regulations, supply chain. Like working in an international, cross-functional team. Learn quickly and actively use new tools, including AI. Focus on outcomes rather than activities. Будет плюсом: Experience with environmental or sustainability data (e.g. life cycle assessments, regulatory sustainability reporting) is a plus