Data engineer in finance

As a Data Engineer in Finance, you build and run the data foundations and products that Siemens Energy Finance uses for reporting, analytics, automation and AI. Our team covers the whole landscape: platform and product delivery, from source systems to consumption. Depending on your strengths and our priorities, your focus can lean towards platform or delivery, source or consumption. You work closely with product owners, data architects, data governance and literacy specialists and Finance stakeholders. It is a hands-on role for an experienced engineer who likes seeing their work used in production.

• Work across the platform (shared ingestion, integration and engineering foundations) and data product delivery (turning Finance requirements into production-grade products).
• Cover the full data flow, from integrating ERP and other source systems via ETL/ELT to well-modelled data for reporting, analytics and AI.
• Build maintainable transformations and data models with Snowflake, SQL, Python and dbt.
• Embed data quality checks, testing, observability and lineage into everything you deliver.
• Use Git (e.g. GitLab, GitHub) and CI/CD for controlled, repeatable deployments.
• Own your work end to end, from design to production operation, and fix recurring failures at their root cause.
• Align with product owners on scope and priorities, and share knowledge through documentation, reusable code and coaching.

• 5+ years in data engineering, building and operating production pipelines, platforms or data products.
• Strong hands-on skills in Snowflake, advanced SQL, Python and dbt.
• Practical experience with Git (e.g. GitLab, GitHub, Azure DevOps), CI/CD and automated testing.
• Sound knowledge of data modelling, database design and performance optimization.
• Ability to turn ambiguous requirements into maintainable solutions and to fix production issues.
• A degree in Computer Science, Engineering or a related field, or a relevant apprenticeship.
• A results-driven, collaborative mindset for a complex, international environment.
• Strong professional English
• Будет плюсом: knowledge of Finance data and processes, semantic modelling, and practical AI skills (e.g. working with LLMs or AI-assisted development); German is a plus

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