Staff Data Engineer
Pubblicato il 19-10-2026 - Abb E-Mobility in San Giovanni Valdarno
As Staff Data Engineer, you will lead the evolution of ABB E-mobility’s data platform and play a key role in scaling our data capabilities across the organization. Working at the center of a federated data ecosystem, you will define the technical foundations, governance standards and engineering practices that enable domain teams to build, own and operate trusted data products while accelerating data-driven decision-making and AI adoption across the business. Your Responsibilities Define and evolve our self-service data platform, enabling domain teams to build, deploy and operate data products independently while ensuring scalability, reliability, security and ease of use across the organization. Provide technical leadership across the data community, driving architecture decisions that balance domain autonomy with interoperability, consistency and long-term platform sustainability in a Data Mesh environment. Design and implement federated governance capabilities, embedding data contracts, schema evolution, data quality controls, access management,
privacy requirements and security policies directly into platform processes and tooling. Build and maintain engineering accelerators and paved paths, including reusable templates, CI/CD frameworks, infrastructure modules and developer tooling that simplify adoption and promote engineering best practices across teams. Own platform performance, reliability and cost optimization, continuously improving operational excellence and ensuring that data services remain scalable, efficient and resilient as adoption grows. Partner with IT and business stakeholders on strategic integration initiatives, providing technical guidance on enterprise data architecture and identifying opportunities to leverage the data platform as a middleware and integration layer. Act as a hands-on data engineering consultant, supporting functions with limited technical capabilities by designing and implementing ingestion patterns, pipelines and data solu