Lead Data Ai Engineer
Pubblicato il 09-08-2026 - Jobtailor in Milano
Responsibilities
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Act as Technical Product Owner (TPO) for AI Data Engineering products, capabilities, and platforms.
Partner with business stakeholders, AI teams, and architects to translate business requirements into scalable data and AI engineering solutions.
Define and prioritize platform backlogs, technical roadmaps, and delivery plans.
Drive adoption of reusable data products, AI services, and platform capabilities across multiple business domains.
Design, develop, and maintain scalable data pipelines supporting AI, Analytics, Machine Learning, and Generative AI use cases.
Lead implementation of batch, streaming, and real-time data integration capabilities.
Build trusted and governed data assets supporting enterprise AI use cases.
Drive engineering standards for data quality, observability, lineage, monitoring, and reliability.
Ensure data platforms are secure, scalable, resilient, and compliant with enterprise standards.
Enable AI solution delivery through feature stores, vector databases, model deployment pipelines, and data services.
Support implementation of Generative AI, LLM, RAG, and Agentic AI architectures through scalable data foundations.
Collaborate with AI Engineers and Data Scientists to operationalize AI solutions.
Establish and maintain MLOps and DataOps practices.
Design and operate cloud-native AI and Data Platforms.
Define architecture patterns for data ingestion, transformation, storage, governance, and consumption.
Optimize platform performance, scalability, reliability, and cost efficiency.
Lead implementation of Infrastructure-as-Code, CI/CD, monitoring, and observability frameworks.
Lead Agile squads delivering AI Data Engineering and platform capabilities.
Facilitate sprint planning, backlog refinement, technical reviews, and delivery governance.
Promote DevOps, DataOps, and Agile engineering best practices.
Act as the bridge between business stakeholders, AI teams, platform teams, architects, and delivery organizations.
Ensure compliance with enterprise security, privacy, data governance, and responsible AI requirements.
Define and monitor OKRs/KPIs related to platform adoption, data quality, delivery velocity, operational performance, and business value realization.
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, Data Engineering, Information Systems, Artificial Intelligence, or related disciplines.
5–7+ years of experience in Data Engineering, AI Engineering, Platform Engineering, or Cloud Data Platform roles.
Proven experience designing enterprise-scale data pipelines and cloud-native data platforms.
Experience acting as Technical Product Owner, Delivery Lead, Lead Engineer, or Squad Lead.
Strong expertise in ETL/ELT, Data Lakes, Lakehouse architectures, Data Warehousing, Metadata Management, and Data Governance.
Hands?on experience with Azure, AWS, or GCP.
Understanding of Generative AI, LLMs, Vector Databases, RAG, and AI agent architectures.
Experience implementing MLOps, CI/CD, Infrastructure-as-Code, and DataOps practices.
Strong SQL and Python skills.
Experience working in Agile and Scrum environments.
Core Competencies
Demonstrates expertise in designing and implementing scalable data pipelines and cloud-native data platforms, with a strong focus on AI and Data Engineering solutions.
xysqume Proficient in MLOps, DataOps, and Agile methodologies to drive platform adoption and ensure data quality and compliance.
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