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.

If you are a full-stack data professional who wants to see the business impact of your work, who takes pride in clean pipelines and well-crafted data apps, and who is actively developing your AI-assisted development capability — this is the

environment for it.

Senior Data Engineer