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.

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.

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.

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.

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.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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.

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

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.

As a Founding Data and ML Engineer, you will design scalable data pipelines, develop AI/ML models, and enhance product performance through applied AI techniques.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Lead migration from Snowflake to the client platform, build AI-native data migration capabilities, design and maintain scalable data pipelines and platforms, propose improvements, and collaborate with senior engineers, product/project managers, and data analysts.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Design and implement AI solutions using ML models and NLP, collaborate with teams, and support business process improvement through AI.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Build and scale a blockchain data platform handling petabytes of on-chain data. Design and implement distributed systems, data ingestion/decoding pipelines, and performant SQL processing. Write production code (Kotlin, Go, Java, Rust), improve developer experience, automate operations (including AI-driven automation), and own platform components.

As a Data Engineer at Camlin, you will design and implement data solutions, support data-related requirements, and enhance tools and applications by addressing issues. Collaboration with cross-functional teams and mentorship of peers is key.