Top Data Engineer Jobs in Italy

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Build scalable data products and pipelines for predictive maintenance, anomaly detection, asset monitoring, and energy forecasting. Collaborate with data scientists, product managers, domain experts, and software engineers to design technical solutions, support APIs and visualizations, prototype ideas, and improve architecture, reliability, and maintainability across cloud-native systems.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Build and maintain cloud-based ELT/ETL pipelines using dbt, Airflow, and Cloud Composer. Develop data ingestion and transformation jobs, implement data quality tests, monitor alerts, maintain lakehouse schemas across Snowflake, BigQuery, and Databricks, document data assets, and collaborate with analytics and data science teams. The role requires strong SQL, foundational Python and Git knowledge, and 2–3 years of relevant experience.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Leads data acquisition, validation, post-processing, and reporting for turbomachinery and climate technology testing. Designs data acquisition architectures, leads test execution teams, troubleshoots experimental data, and collaborates on PLC-based control systems, automation, instrumentation, communication networks, and software integration. Supports LabVIEW development, control logic, interlocks, alarms, and closed-loop algorithms while coordinating with engineers, suppliers, and customers.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Develop and optimize large language models and NLP capabilities for decision support, mission planning, workflow automation, and situational awareness. Responsibilities include data preprocessing, model training and fine-tuning, RAG and agentic system development, evaluation, deployment, monitoring, documentation, and collaboration with engineering and product teams. The role requires strong Python, deep learning, LLMOps, cloud, and MLOps expertise, plus knowledge of responsible AI and bias mitigation.

Leads mechanical engineering design and technical delivery for complex data centre projects. Responsibilities include designing cooling, ventilation, automation, controls, and ancillary systems; coordinating with electrical and multidisciplinary teams; supervising engineers; producing and checking detailed designs; managing resources and deadlines; and maintaining client relationships. The role also involves applying building physics, CFD, sustainable design principles, CAD, BIM, and digital design tools.

Lead mechanical engineering design and delivery for complex data centre projects. Responsibilities include designing cooling, ventilation, automation and control systems; coordinating with electrical and multidisciplinary teams; supervising engineers; producing and checking technical designs; managing resources and deadlines; and representing Ramboll with clients. The role requires experience with mission-critical systems, strong communication, engineering qualifications, professional registration progress, and familiarity with digital and parametric design tools.

Designs, develops, deploys, operationalizes, and maintains enterprise-scale data pipelines, workflows, and analytic solutions. Processes structured and unstructured data from disparate sources using programming and querying languages, collaborates with multidisciplinary Agile teams, and guides analysts, engineers, developers, and data consumers. The role supports mission-driven client projects and requires up to 25% travel and Secret clearance.

Design, train, evaluate, and optimize machine learning, computer vision, and deep learning models for edge devices and cloud backends. Prepare and validate large-scale datasets, optimize models for hardware constraints, write production-quality software, integrate models with embedded and cloud systems, monitor production performance, and research emerging AI technologies.

Lead the technical direction and evolution of the organization’s shared Data Lakehouse. Design data models, ingestion and transformation patterns, and scalable data products that support analytics and AI. Translate business needs into engineering priorities, manage the lakehouse roadmap, align stakeholders, ensure data quality and governance, and provide technical leadership to data engineers. Improve platform usability, performance, reliability, maintainability, scalability, and cost efficiency.

Build and deploy Salesforce Data 360 solutions in enterprise customer environments. Develop ingestion pipelines, data models, identity resolution, integrations, retrieval systems, vector search, and AI-agent communication protocols. Write SQL and implementation code, test at scale, troubleshoot production issues, apply security controls, and support deployment and handoff. Collaborate directly with architects, customers, partners, and technical teams while documenting solutions, tradeoffs, and platform gaps.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Design, build, and maintain scalable data pipelines and integrations on cloud platforms for financial-services clients. Collect, process, transform large structured and unstructured datasets, support ETL/ELT automation, ensure data quality and governance, and prepare datasets for reporting and analytics while collaborating with technical and business stakeholders.

Own the full lifecycle of pricing data products and pipelines, partnering with data scientists to productionize real-time machine learning algorithms. Architect scalable data and ML infrastructure, manage performance, lead cross-team infrastructure initiatives, build enterprise data warehouses and marts, orchestrate production workflows, and deliver tested, maintainable code. The role also involves cloud infrastructure, DevOps, dashboarding, GDPR compliance, and communicating technical concepts to business stakeholders.

Work with manufacturing, test, and production teams to analyze production data, improve yield, develop and optimize test procedures, support failure analysis and root-cause investigations, automate data workflows, manage SQL-based data, and support product industrialization and non-conformity resolution.

Lead analytics for engineering operations: drive demand vs capacity analysis, cost and outsourcing optimization, implement earned value management, apply statistical data analytics to identify process drivers, and communicate insights to stakeholders to enable data-driven decisions.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

As a Data Engineer, you will design and maintain data pipelines, ensure data quality, optimize database performance, and collaborate with data teams to enhance data accessibility and processing.