Senior Data Engineer And Data Scientist

At ABB, we help industries outrun - leaner and cleaner. Here, progress is an expectation - for you, your team, and the world. As a global market leader, we’ll give you what you need to make it happen. It won’t always be easy, growing takes grit. But at ABB, you’ll never run alone. Run what runs the world.

This Position reports to:

In this role, you will have the opportunity to shape the future of data-driven decision making and AI innovation within ABB. You will contribute to building robust data infrastructure and analytics solutions, and maintaining cutting-edge generative and agentic AI systems that empower the Digital Agile Unit and the Data Insight team to deliver actionable insights and new AI features. Each day, you will apply your expertise in data engineering, data science, and AI to design scalable data pipelines, optimize data workflows, maintain LL‑based solutions, and enable advanced analytics that drive strategic business decisions.

The work model for the role is: hybrid.

Location:

Italy or Switzerland

This role is contributing to the Digital Platforms & Capabilities Agile Unit in Electrification Smart Power.

• Design and Build Scalable Data Pipelines
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  Develop and maintain robust data engineering solutions using Databricks and PySpark to process, transform, and integrate large-scale datasets from multiple sources.
• Enable Data-Driven Decision Making
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  Create and optimize data models, analytics frameworks, and reporting solutions that provide actionable insights to business stakeholders and leadership teams.
• Maintain and Optimize Generative AI Solutions
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  Oversee the deployment, monitoring, and continuous improvement of LLM-based applications, including generative and agentic AI systems. Ensure reliability, performance, and cost-effectiveness of AI solutions in production.
• Develop and Refine AI Agents
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  Build and maintain intelligent agents that leverage Large Language Models for automated decision‑making, data analysis, and business process optimization. Implement prompt engineering strategies and fine‑tuning approaches to enhance agent performance.
• Lead End-to-End Data Projects
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  Take ownership of data initiatives from requirements gathering and architecture design through implementation, testing, and deployment in production environments.
• Collaborate Across Functions
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  Work closely with business analysts, data scientists, product owners to understand data needs and deliver solutions that support strategic decision-making processes.
• Promote Best Practices and Innovation
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  Champion data engineering and AI excellence, implement Data Ops and MLOps practices, mentor team members, and stay current with emerging technologies in the data engineering, analytics, and generative AI.

Our teams support each other, collaborate, and never stop learning. Everyone brings something unique, and together we push ideas forward to solve real problems. Being part of our team means your work matters - because the progress we make here creates real impact out there.

• MSc or PhD in Computer Science, Data Engineering, Computer Engineering, Applied Mathematics, Statistics, or a related field.
• Minimum 5 years of experience in data engineering and/or data science roles, preferably in industrial, energy, or technology sectors.
• Expert-level proficiency in Databricks and PySpark for building distributed data processing pipelines. Strong command of Python and SQL for data manipulation, transformation, and analysis.
• Proven experience with Large Language Models (LLMs):
  Hands‑on experience deploying, maintaining, and optimizing generative AI solutions. Proficiency in prompt engineering, RAG (Retrieval‑Augmented Generation) architectures, and agentic AI frameworks (e.g., Lang Chain, Llama Index, Auto Gen).
• Experience with cloud data platforms (e.g., Azure, AWS), data warehousing concepts, ETL/ELT processes, and modern data stack tools. Familiarity with data orchestration tools (e.g., Apache Airflow) is a plus.
• Knowledge of data modeling, data governance, and analytics best practices. Experience with…