Top Data Engineer Jobs in Italy

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.

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, build, and operate scalable data pipelines and models on GCP. Ingest, transform, orchestrate, and ensure data quality and reliability for analytics and ML. Collaborate with cross-functional data teams, translate business requirements into deliverables, and mentor junior engineers while following development and change-management standards.

Build, maintain and improve Moneyfarm's Redshift + Airflow + dbt data platform for marketing acquisition. Design ingestion, orchestration, transformations, analytical models and ML deployments. Reconcile raw data from multiple sources, collaborate with analysts, data scientists, engineers and marketers, and produce artifacts usable by humans and AI tools to drive acquisition insights and business value.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

As a Data Engineer, you will build and maintain data pipelines, improve data warehousing, and enable data-driven decisions by collaborating with engineering, data science, and analyst teams.

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.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and maintain scalable data pipelines, warehouses and lakes; ensure data quality, security, and performance; enable analytics/ML deployment; collaborate across multi-site teams and support governance and delivery.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

Architect and build high-performance spatial data pipelines and scalable backend logic for massive datasets. Optimize geometry storage, implement spatial indexing, work with OLAP databases, and lead a technical team through code reviews, mentoring, and technical leadership.

Design, build, and maintain scalable data pipelines and platforms for analytics. Develop workflows to ingest, parse, and process structured and unstructured data. Collaborate with cross-functional teams to support reporting, analytics, and mission-driven use cases.

The role involves managing data transformation and reporting pipelines, automating data processes, and performing data analysis while collaborating with business owners. The candidate will use tools like Power BI and SQL in their tasks.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

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.

As a Senior Data Engineer, manage and develop complex data applications, create ETL workflows, and ensure data platforms are scalable while collaborating with a multi-disciplinary team.

Design, build, and maintain scalable data pipelines and platforms. Collaborate with cross-functional teams to retrieve, parse, and process structured and unstructured data for analytics, reporting, and mission-driven use cases. Guide implementation and deployment of data engineering solutions in agile environments.

Design, build, and maintain scalable data pipelines and database systems using Microsoft Azure (Data Factory, Azure SQL, Cosmos DB). Support data workflows for products and AI initiatives, ensure data quality, performance, security, and collaborate with engineering and product teams.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.

The Software Engineer will enhance data collection for AI model training, manage cloud infrastructure, and collaborate across teams to improve data quality and scale.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Design, build and maintain Zero-ETL scalable data pipelines and Redshift data models. Develop and manage Airflow workflows, integrate multiple data sources, ensure data quality and availability, optimize queries and warehouse performance, monitor and resolve platform issues, document pipelines and collaborate with analysts and stakeholders on architecture and analytics needs.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.