Siemens Energy

Senior Data Engineer

·       Senior Data Engineer Job Family Information Technology Experience Level Senior Professional Location India Language English required Reports to Senior Manager, Product Engineering — Digital Finance, Transformation of Industries

A Snapshot of Your Day

·       You start the morning reviewing a pull request from your last sprint — a new Snowflake view feeding the Business Controlling variance dashboard.

·       You added AI-assisted test generation to validate the data transformations overnight; the coverage looks good, and you close it out before the stand-up.

·       Mid-morning, you pick up requirements from the Product Owner for a Streamlit application that will let Commercial Project Managers explore project margin trends interactively.

·       You sketch the data model, identify two upstream SAP tables that need transformation logic in Python, and draft a quick design spec before coding begins. You use your AI coding assistant throughout — generating boilerplate, suggesting edge case handling, and accelerating documentation so the code is production-ready, not just functional.

·       In the afternoon, you flex into BI development to support a Power BI release your colleague is finalizing.

·       A calculated measure isn’t behaving as expected; you trace it back to a semantic layer definition in the shared dataset and fix it at the source. Clean data in, clean report out.

·       No two days look identical. But the throughline is always the same: you build things end to end, you build them well, and you bring the curiosity and craft that raises the bar for the whole team.

About the Role

·       The Siemens Energy, Transformation of Industries Digital Finance team is executing a multi-year strategy to transform how 1,000+ finance team members work — replacing manual data consolidation and reactive reporting with automated, AI-powered intelligence delivered through a unified platform.

·       The strategy is defined, the products are in active delivery, and the team is accelerating.

·       The Senior Data Engineer is a full-stack data professional who builds across the entire solution layer: from Snowflake pipelines and Python automation on the backend, to Power BI semantic models and Streamlit applications at the point of user interaction.

·       This is not a siloed role. You own the full data journey — ingestion, transformation, modeling, and presentation — and you bring the discipline to build each layer to a production standard.

·       You will join a distributed team operating across India, Romania, and Germany, working in two-week agile sprints.

·       You will report to the Senior Manager of Product Engineering, who owns the engineering standards, DevOps practices, and AI-assisted development adoption that govern how every product is built.

·       This role is co-located with that manager in India. You bring pro-code depth, AI-assisted development fluency, and the range to flex into BI development capacity when sprint priorities demand it.

·       You will help raise the engineering floor across the team.

How You’ll Make an Impact

·       Build and maintain data pipelines and backend infrastructure Design, build, and maintain scalable data pipelines in Snowflake — ingestion, transformation, and semantic layer — using SQL and Python as primary development languages.

·       Develop and maintain Python-based automation and ETL workflows, replacing fragile legacy Alteryx and UiPath processes with resilient, version-controlled solutions.

·       Build and maintain clean, well-documented data models that serve as the trusted foundation for BI reporting and AI product development. Implement and adhere to GitLab version control standards for 100% of development work — branching, pull requests, code reviews, and release management without exception.

·       Write unit and integration tests as a default practice, with AI-assisted test generation as the standard tooling approach.

·       Develop frontend data applications and BI solutions Build production-grade Streamlit applications that give finance users direct, interactive access to data — designing for usability, performance, and maintainability.

·       Develop and maintain Power BI reports and datasets, including DAX measures, semantic layer configuration, and certified dataset management in the shared workspace.

·       Collaborate with the UI/UX Designer to implement designs that meet the team’s product experience standards, ensuring the frontend reflects the quality of the data beneath it.

·       Flex into BI development capacity when sprint priorities demand it — functioning as a reliable generalist across the full visualization layer, not just your primary workstream.

·       Adopt and accelerate AI-assisted development Use AI coding tools (e.g., GitHub Copilot, Cursor, or equivalent) actively and deliberately in daily development — for code generation, test case creation, documentation, and boilerplate acceleration.

·       Participate in the team’s transition from AI-assisted to AI-generated development, contributing to shared standards and coaching peers on effective tooling use. Contribute to the team’s 25% story velocity increase target by compressing time from requirements to production through disciplined AI tooling adoption.

·       Collaborate across roles and domains Partner with the Data Architect on data modeling decisions, semantic layer design, and alignment with the broader TI data strategy and Community Data Pool integration.

·       Work directly with Product Owners to translate finance stakeholder requirements into clear, implementable technical solutions — asking the right clarifying questions before building, not after.

·       Contribute to sprint planning and backlog refinement with engineering estimates that reflect realistic build complexity, surfacing risks before they become delivery problems.

·       Document solutions in GitLab and the team handbook as a matter of craft — run books, data dictionaries, and deployment notes that make every solution maintainable by the team, not just by you.

What You Bring

·       Strong proficiency in SQL and Python for data pipeline development, transformation logic, and automation — writing production-quality code.

·       Hands-on experience with Snowflake or an equivalent cloud data warehouse platform, including data modeling, performance optimization, and semantic layer design.

·       Proven ability to build interactive data applications in Streamlit or a comparable Python-based framework, from prototype to production. Working proficiency in Power BI — report development, DAX measures, semantic model configuration, and dataset management in a shared workspace.

·       Experience with GitLab or equivalent version control tools: branching strategy, pull requests, code reviews, and CI/CD pipeline basics. Familiarity with SAP data structures (S/4HANA, EPM+, or SAP Analytics Cloud) is a meaningful advantage in this environment.

·       AI-assisted development Demonstrable experience using AI coding tools (GitHub Copilot, Cursor, or equivalent) in active development work — not just experimentation, but a visible trajectory of adoption. A clear growth mindset around AI-generated development — you understand where these tools accelerate delivery and where human judgment is required, and you actively build both.

·       Finance domain and stakeholder orientation Prior exposure to finance data environments is preferred — familiarity with financial reporting structures, controlling logic, or FP&A data models will accelerate your impact significantly.

·       Ability to work directly with non-technical finance stakeholders, translating business requirements into data solutions without requiring an intermediary. Comfort engaging with finance users to validate outputs — you understand that data accuracy is not a backend concern alone; it is the point of everything you build.

·       Ways of working Experience working in agile delivery environments — sprint planning, stand-ups, demos, and retrospectives as a default operating rhythm, not an imposed structure.

·       A documentation discipline that is genuinely internalized — you document because it makes the work better, not because it is required. Strong written English communication skills; the team operates across six countries and five time zones, and clear asynchronous communication is a core competency. A collaborative, low-ego working style that thrives in a distributed team — generous with knowledge, direct with feedback, and committed to raising the standard for everyone.

Qualifications

·       Bachelor’s or master’s degree in computer science, Data Engineering, Information Systems, or a related technical field.

·       5+ years of professional experience in data engineering, analytics engineering, or a closely related role, with demonstrated growth in scope and technical depth.

·       Open-minded, eager to learn, and dedicated to continuous self-improvement — in a team that is actively raising its own bar, you will be expected to grow with it.

Why This Role

·       This is not a maintenance role.

·       The products being built here — automated finance intelligence, AI-powered insights, self-service data applications — are in active delivery and accelerating.

·       The team is in the middle of a deliberate transformation: new engineering standards, new tooling, new ways of working.

·       You will not be inheriting a fully formed practice; you will be helping to build it.