Data Engineer

• Supportare lo sviluppo e la manutenzione di pipeline di ingestione, trasformazione e distribuzione dei dati (ETL/ELT) utilizzando Python e framework di data processing.
• Collaborare allo sviluppo e all’ottimizzazione di job Apache Spark su piattaforme come Databricks.
• Integrare diverse sorgenti dati (database, API, file system) utilizzando servizi cloud come AWS S3, Glue e Redshift.
• Scrivere codice in Python e/o Java seguendo buone pratiche di sviluppo, documentazione e testing.
• Contribuire alla gestione di workflow e pipeline dati utilizzando strumenti di orchestrazione come Apache Airflow o Databricks Workflows.
• Collaborare con i team Data Science, BI e Data Architecture nella preparazione e gestione dei dati.

• Sviluppare, testare e mantenere pipeline di dati
• Implementare e ottimizzare Job Spark su Databricks
• Integrare sorgenti dati eterogenee utilizzando i servizi AWS
• Collaborare con i team di Data Architect, Data Science e BI

Senior Data Engineer

• Progettare e mantenere l’architettura della piattaforma dati su AWS e Databricks;
• Definire standard e linee guida architetturali;
• Disegnare e supervisionare pipeline di ingestione, trasformazione e distribuzione dei dati;
• Guidare la scelta delle tecnologie e dei pattern architetturali;
• Sviluppare componenti e prototipi in Java e Python;
• Collaborare con i team di Data Engineering, Data Science, BI e Product;
• Presidiare aspetti di sicurezza e compliance;
• Valutare e introdurre nuove tecnologie e framework.

Senior Data Architect

• Engage in collaborative efforts with the business teams to comprehend data requirements
• Develop robust architectures and data models
• Enhance business processes managing data and AI models
• Build and maintain robust data pipelines
• Maintain and improve existing production processes
• Contribute to R&D initiatives to discover and integrate solutions

• 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.

Lead Data AI Engineer

• Collect and prioritize data requirements (sources, schemas, real-time needs, integrations, AI/ML readiness) and articulate them to DBS and Global D&A
• Represent Global Customer & Consumer Experience in DBS tooling selection, implementation, and roadmap discussions
• Drive Global D&A governance implementation participate in planning forums to secure platform capacity and features for growth and AI workloads
• Serve as primary liaison between Growth CX, DBS, and Global D&A
• Coordinate schema design, event tracking standards, integration testing, and enable channel performance data flows and AI/ML readiness
• Own data governance and compliance across the martech-to-platform chain
• Track key data metrics (completeness, freshness, latency, errors, AI readiness)
• Align with Data Product & Data Intelligence Managers to promote guidelines

Data Platform and Governance Manager

• Build and manage reliable data pipelines involving ingestion/collection, processing, integration, storage, and data availability across the organization
• Work within a distributed systems architecture for massively parallel (MPP) data processing, combining diverse heterogeneous data sources and collaborating with analytics and data science teams to build solutions and generate data-driven value

• - Design, build, and optimize scalable data pipelines to power our AI operational initiatives.
• - Implement and manage data flows tailored for **agentic workflows** and **LLM interactions**.
• - Maintain and evolve our data infrastructure, ensuring high data quality and availability.
• - Partner with the AI team to deploy smart, efficient, and automated solutions.

Data Engineer jobs in Italy

See all jobs on JobTailor

Data Engineer

Senior Data Engineer

Senior Data Architect

Data Engineer

Lead Data AI Engineer

Data Platform and Governance Manager

Senior Data Engineer

Data Engineer – Freelance