Staff Analytics Engineer
Pubblicato il 19-10-2026 - Abb E-Mobility in San Giovanni Valdarno
As Staff Analytics Engineer, you will play a pivotal role in shaping the foundations of ABB E-mobility’s data ecosystem by driving scalable analytics engineering practices, strengthening data governance and enabling a federated data community to make better, faster and more trusted decisions. Acting as a technical leader and community enabler, you will help democratize data modeling, harmonize business metrics and accelerate the adoption of AI-powered analytics across the organization.
Tutti i candidati sono invitati a leggere attentamente la seguente descrizione del lavoro e le relative informazioni prima di candidarsi.
Your Responsibilities
Define and evolve the company’s analytics engineering architecture, ensuring scalable, maintainable and interoperable data models that support both business analytics and future AI initiatives across a federated data landscape.
Drive the analytics engineering strategy and tooling roadmap, partnering closely with the Staff Data Engineer to establish standards,
frameworks and best practices that make high-quality data modeling accessible and scalable for the wider data community.
Lead the standardization and harmonization of business metrics, creating shared definitions and modeling conventions that improve consistency, trust and alignment across functions, domains and reporting use cases.
Establish and continuously improve data governance and data quality practices, implementing monitoring frameworks, quality controls and accountability models that ensure reliable and trusted data across all domains.
Coach, mentor and enable data practitioners across the organization, providing training, guidance and hands‑on support to data analysts and domain teams while fostering a strong data community and culture of collaboration.
Deliver complex data modeling and engineering solutions hands‑on, developing and optimizing data pipelines, supporting teams that require additional expertise and ensuring efficient, scalable and high‑perfor