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.

Design, implement, and maintain highly available data pipelines supporting cybersecurity products. Develop medium to large software features, reduce technical debt, collaborate in an agile team, automate processes, and own user-facing features from inception through release. The role emphasizes full-stack development, data engineering, AI, and NLP, with opportunities to use Python or Ruby, Docker, JavaScript, and React.

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.

Lead the architecture and development of a multi-country Snowflake and GCP data platform. Build CDC ingestion pipelines, dbt semantic models, and DataOps CI/CD workflows; implement governance, RBAC, PII masking, compliance, FinOps, and performance optimization. Partner with business stakeholders, document architectural decisions, and mentor data engineers across LATAM operations.

Owns low-latency market-data infrastructure for crypto, futures, FX, DeFi, and prediction markets. Responsibilities include building feed handlers, sequencing and recovering data, reconstructing order books and blockchain state, optimizing latency, capturing and replaying full-depth data, and implementing data-quality checks. The role requires operating resilient systems around the clock in an on-call rotation.

Design and deliver scalable data products for predictive maintenance, anomaly detection, asset monitoring, and energy forecasting. Build cloud-native pipelines and reliable data systems using Databricks, PySpark, Spark, SQL, and AWS. Partner with data scientists, product managers, and domain experts to translate ambiguous problems into technical solutions. Lead architecture decisions, mentor engineers, improve operational reliability, and support customer-facing products across the full data lifecycle.

Develop and deploy scalable, production-grade machine learning and AI solutions for financial research. Build LLM- and NLP-based applications, collaborate with senior engineers, and contribute to reliable AI-driven products. Responsibilities require Python, software engineering, deep learning, databases, Docker, and at least one major deep learning framework. Preferred experience includes RAG pipelines, vector databases, LLM fine-tuning, cloud platforms, and Kaggle competitions.

Develop data-driven product capabilities for asset monitoring, predictive maintenance, forecasting, and optimization. Responsibilities include analyzing complex datasets, building statistical and machine learning models, developing reliable data pipelines, integrating solutions into products, evaluating performance, and communicating technical trade-offs across engineering, product, and domain teams.

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.

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.

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.

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.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

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.