Emea Data Engineering Lead
hace 2 semanas
**About the Role**:
We are seeking a passionate and experienced EMEA Data Engineering lead. In this role, you will be responsible for building and growing a high-performing team to ensure the successful delivery and execution of the data projects, capabilities and solutions to support EMEA countries business function at Chubb.
**Responsibilities**:
- **Excellence execution and Strategic Leadership**:
- Design and implement a long-term roadmap of Data projects and capabilities for EMEA aligned with the overall business strategy.
- Provide leadership to the data engineering domain.
- Partner with stakeholders across the organization to understand their data needs and translate them into actionable data engineering solutions.
- Develop and maintain a strong relationship with key decision-makers to secure resources and support for the team.
- Stay up-to-date on the latest trends and innovations in data engineering and identify opportunities to leverage them within the company.
- Effectively prioritise and deliver to a clear roadmap of deliverables.
- **People Management**:
- Build and manage a team of talented data engineers with the ability to not only keep up with, but also pioneer, in this space
- Foster a culture of collaboration, innovation, and continuous learning within the team.
- Ability to collaborate effectively in a multicultural team environment.
- **Technical Expertise**:
- Possess deep knowledge of data engineering principles, tools, and technologies, including data modeling experience (e.g., data pipelines, data lakes, cloud platforms, big data frameworks).
- Experience with Cloud services, preferably Azure and its data engineering services (ADF, Databricks, Synapse, Cosmos etc).
- Strong experience with Data platforms, Snowflake is a strong plus.
- Design and implement scalable and secure data architectures that meet the needs of the business.
- Ensure the quality, reliability, and maintainability of data pipelines and data solutions.
- **Communication and Collaboration**:
- Effectively communicate technical and non-technical concepts to both technical and non-technical audiences.
- Collaborate effectively with stakeholders across different departments (e.g., business intelligence, product, analytics) to understand and meet their data needs.
- Present complex data engineering concepts in a clear and concise manner.
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