Lead Data AI Engineer
Pubblicato il 09-08-2026 - Jobtailor in Milano
ph3Responsibilities /h3 ul liAct as Technical Product Owner (TPO) for AI Data Engineering products, capabilities, and platforms. /li liPartner with business stakeholders, AI teams, and architects to translate business requirements into scalable data and AI engineering solutions. /li liDefine and prioritize platform backlogs, technical roadmaps, and delivery plans. /li liDrive adoption of reusable data products, AI services, and platform capabilities across multiple business domains. /li liDesign, develop, and maintain scalable data pipelines supporting AI, Analytics, Machine Learning, and Generative AI use cases. /li liLead implementation of batch, streaming, and real-time data integration capabilities. /li liBuild trusted and governed data assets supporting enterprise AI use cases. /li liDrive engineering standards for data quality, observability, lineage, monitoring, and reliability. /li liEnsure data platforms are secure, scalable, resilient, and compliant with enterprise standards. /li liEnable AI solution delivery through feature stores, vector databases, model deployment pipelines, and data services. /li liSupport implementation of Generative AI, LLM, RAG, and Agentic AI architectures through scalable data foundations. /li liCollaborate with AI Engineers and Data Scientists to operationalize AI solutions. /li liEstablish and maintain MLOps and DataOps practices. /li liDesign and operate cloud-native AI and Data Platforms. /li liDefine architecture patterns for data ingestion, transformation, storage, governance, and consumption. /li liOptimize platform performance, scalability, reliability, and cost efficiency. /li liLead implementation of Infrastructure-as-Code, CI/CD, monitoring, and observability frameworks. /li liLead Agile squads delivering AI Data Engineering and platform capabilities.
/li liFacilitate sprint planning, backlog refinement, technical reviews, and delivery governance. /li liPromote DevOps, DataOps, and Agile engineering best practices. /li liAct as the bridge between business stakeholders, AI teams, platform teams, architects, and delivery organizations. /li liEnsure compliance with enterprise security, privacy, data governance, and responsible AI requirements. /li liDefine and monitor OKRs/KPIs related to platform adoption, data quality, delivery velocity, operational performance, and business value realization. /li /ul h3Requirements /h3 ul liBachelor's or Master's degree in Computer Science, Engineering, Data Engineering, Information Systems, Artificial Intelligence, or related disciplines. /li li5–7+ years of experience in Data Engineering, AI Engineering, Platform Engineering, or Cloud Data Platform roles. /li liProven experience designing enterprise-scale data pipelines and cloud-native data platforms. /li liExperience acting as Technical Product Owner, Delivery Lead, Lead Engineer, or Squad Lead. /li liStrong expertise in ETL/ELT, Data Lakes, Lakehouse architectures, Data Warehousing, Metadata Management, and Data Governance. /li liHands‑on experience with Azure, AWS, or GCP. /li liUnderstanding of Generative AI, LLMs, Vector Databases, RAG, and AI agent architectures. /li liExperience implementing MLOps, CI/CD, Infrastructure-as-Code, and DataOps practices. /li liStrong SQL and Python skills. /li liExperience working in Agile and Scrum environments. /li /ul h3Core Competencies /h3 pDemonstrates expertise in designing and implementing scalable data pipelines and cloud-native data platforms, with a strong focus on AI and Data Engineering solutions. Proficient in MLOps, DataOps, and Agile methodologies to drive platform adoption and ensure data quality and compliance. /p /p #J-18808-Ljbffr