Top Data Engineer Jobs in Italy

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.

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.

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.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

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.

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.

Build and optimize scalable data pipelines supporting AI operational efficiency initiatives. Design data flows for agentic workflows and LLM interactions, maintain reliable modern data infrastructure, ensure data quality and availability, and partner with the AI team to deploy automated solutions. The role requires strong Python, dbt, Airflow, and modern data warehouse experience, plus familiarity with GenAI architectures such as LangChain, LlamaIndex, and RAG.

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.

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.

Designs, develops, operationalizes, and maintains enterprise-scale data applications, pipelines, and workflows for structured and unstructured data. Collaborates with analysts, developers, engineers, and data consumers in an agile environment to build analytic solutions supporting mission-driven clients. Responsibilities include data retrieval, parsing, processing, assessment, design, implementation, and maintenance.

Lead the design, implementation, and operation of enterprise reporting and analytics platforms. Build scalable pipelines, master data structures, lakehouse and warehouse models, Power BI semantic models, dashboards, and cost-rate databases. Ensure data quality, governance, security, lineage, and auditability while supporting DevOps, deployment, monitoring, and production operations. Partner with finance, operations, and senior stakeholders to deliver trusted data products and business outcomes across complex enterprise systems.

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.

Develop, test, debug, and optimize embedded firmware for Data Center SSD controllers and memory systems. Troubleshoot and perform root-cause analysis, collaborate with cross-functional teams on integration and validation, participate in code reviews, and support performance profiling and failure analysis.

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.

Build, operate, and optimize scalable ETL/ELT data pipelines and data warehouses across cloud platforms. Ensure data quality, lineage, security, and governance; integrate diverse sources; enable AI/ML and analytics via APIs, CI/CD, and orchestration. Collaborate with architects, data scientists, PMO, and privacy teams to support global automotive knowledge systems.

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.

Develops and maintains enterprise applications supporting market data and derivatives. Responsibilities include analyzing functional and regulatory requirements, creating technical documentation, participating in testing and releases, and providing application support and operational oversight. The role requires Java and enterprise development expertise, database and automation tool experience, application security knowledge, and familiarity with financial services regulations such as EMIR.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Design, implement, and test cloud-based server-side software in Python. Optimize code performance, maintain unit and integration tests, and work with AWS, Docker, DevOps, and CI/CD tools. Preferred experience includes reverse engineering public APIs, PostgreSQL, Django ORM and DRF, network protocols, and web or mobile technologies.