Tech Lead - Data & AI

Within Data, AI Tech Solutions & IT Applicationsfunction in Generali Investments Holding, we are looking for atalentedandproactiveresource to join the team. AsTech lead – Data & AI, you will be responsible for development, maintenance, and enhancement of Generali Investments Holding’s cloud data architecture to ensure the integrity, efficiency, and accessibility of our data. You willguide a small multidisciplinary engineering team(Data & Software Engineers, Frontend Developer, DevOps Engineer) and collaborate closely with Group and local teams includingCloud, Security, and Data & AIfunctions. You will work withenterprise-grade technologies, definebest practices across data engineering and software development, and play a key role in shapingnext-generation Data & AI and GenAI solutions within a complex and regulated financial environment

Key Responsibilities

• Design and develop end-to-end data pipelines for efficient and reliable data ingestion, transformation, exposure, and visualization, ensuring consistency and accuracy.
• Design, maintain and evolve data models to support analytical and reporting requirements. 
• Implement data quality controls and monitoring processes to ensure the accuracy and reliability of data and contribute to the development and enforcement of data governance policies. 
• Maintain and continuously improve the Cloud Data Platform, monitoring KPIs, security alerts, performance, and costs, and optimizing ETL processes.
• Troubleshoot and resolve data pipeline and platform issues, performing root-cause and cost/performance analyses.
• Lead and mentor a small engineering team, ensuring alignment with project requirements, timelines, and architectural standards.
• Define and implement GenAI and LLM-based architectures (e.g. RAG, Agents), integrating AI solutions into business processes effectively and safely.
• Establish and enforce a coherent Data & AI governance framework, balancing innovation, compliance, and risk management.
• Collaborate closely with data analysts, data scientists, and business stakeholders to translate requirements into robust technical solutions.
• Promote a data-driven culture through effective communication and cross-department collaboration.