Data Engineer – AI & Data Products

Milano
Tempo indeterminato
Full time

1 mese fa

The Data Technologies, Analytics & BI Solutions team’s mandate is to design, build, and operate scalable Data, AI, and Cloud products and platform services, engineered for reliability, reuse, and long-term sustainability. The team mission is produce fully engineered, product-oriented platforms that enable measurable and repeatable business value at scale. The team owns the end-to-end engineering foundations required for industrialization: reference architectures, shared platforms, data governance enablers, AI engineering practices, reliability standards, security controls, performance optimization, and cost efficiency.Operating within a tech-inspired model, the team collaborates closely with other IT internal structures (such as Innovation & AI Forge) while maintaining a clear mandate: transforming validated initiatives into robust, operable, and economically sustainable products, with the clear objective of ensuring that what reaches production is scalable, maintainable, and aligned with the long-term digital engineering backbone required to support growth, adoption, and financial sustainability.The selected candidate will:

Design, build, and evolve scalable cloud-native data pipelines supporting analytics, AI use cases, data products, and digital applications.
Develop and optimize batch and streaming data ingestion frameworks, integrating structured, semi-structured, and unstructured data into modern data lakehouse architectures.
Implement data quality, observability, and data reliability frameworks, ensuring trust, lineage, and compliance by design.
Enable AI and ML workflows by:

Designing and maintaining feature stores
Automating data preprocessing pipelines
Managing high-quality training and inference datasets
Supporting GenAI and advanced AI use cases with optimized data access patterns
Build backend data services and APIs to support AI-powered and digital solutions in production environments.
Collaborate with AI Engineers and Data Scientists to industrialize models through MLOps best practices, CI/CD pipelines, and scalable deployment patterns.
Work closely with Platform Engineering and SRE teams to ensure reliable deployment, monitoring, scalability, and cost optimization of data and AI workloads.
Contribute to the evolution of the Cloud Data Platform architecture, optimizing storage, compute engines, and distributed processing frameworks.
Ensure security, privacy, and regulatory compliance, especially in handling sensitive financial data.
Provide technical guidance on emerging trends such as GenAI integration, real time analytics, data mesh architectures, and scalable AI platforms.
Coordinate with internal and external partners in delivering complex data, AI, and digital initiatives.

3+ years of experience in Data Engineering or Machine Learning Engineering.
Strong proficiency in Python (Pandas or similar, PySpark), SQL, or Scala for data manipulation and transformation.
Experience with cloud platforms such as AWS, Azure, or Google Cloud and data storage technologies (Redshift, DynamoDB, BigQuery, etc.).
Hands-on experience with distributed computing frameworks (Spark, Ray, etc.).
Solid understanding of data modeling, data contracts, and governance frameworks.
Experience with MLOps and AI lifecycle tools (e.g., MLflow, Kubeflow, Vertex AI, SageMaker).
Experience with MLOps and AI lifecycle tools (e.g., MLflow, Kubeflow, Vertex AI, SageMaker).
Bachelor’s degree in Computer Science, Engineering, or a related field.

Nice to have:

Master’s degree in Engineering, or a related field, or PhD in Computer Science.
Experience in the financial sector.
Familiarity with BI tools such as Power BI, Tableau, or Looker.
Understanding of regulatory requirements in financial data governance.

Generali

Candidati