Top Data Engineer Jobs in Italy

Build a reusable Python ingestion framework to replace legacy Java ETL, develop real-time PostgreSQL-to-Snowflake streaming and CDC pipelines, and improve customer onboarding with LLM-assisted schema mapping. Own production deployment, monitoring, data quality, recovery, and secure handling of sensitive data. Collaborate with the Data and AI Platform team to design infrastructure and make pragmatic architectural decisions.

Design and develop data models for electrical assets, hierarchies, measurements, events, and relationships. Translate engineering knowledge into SQL and Python transformations, calculations, validation rules, and reusable analytical logic for sensor, time-series, telemetry, and operational data. Collaborate with engineering, data science, and software teams to support energy infrastructure products while owning modeling approaches, data quality, validation, and documentation.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Build and maintain legal data infrastructure and document-processing pipelines, including OCR, text extraction, metadata tagging, anonymization, monitoring, alerting, and data-quality checks. Optimize systems for scale and reliability while securing sensitive user data. Collaborate with Product, AI Engineering, and Legal Engineering, owning production pipelines end to end. The role requires strong Python and ETL experience, database and cloud infrastructure expertise, and fluency in English and Italian.

Build and improve large-scale data migration processes, troubleshoot data quality issues, develop SQL and dbt transformation rules, maintain automated reconciliation controls, and support ETL/ELT pipelines. Collaborate with technical and business teams to investigate defects, implement fixes, manage Git-based workflows, and improve migration reliability through testing, CI/CD, documentation, and process industrialization.

Build and optimize scalable batch and real-time data pipelines, ETL/ELT workflows, backend services, and APIs. Translate data models into efficient implementations, ensure data quality through validation and monitoring, troubleshoot workflows, and contribute to tooling and orchestration decisions. Collaborate with architects, data scientists, analytics, and software teams while maintaining strong testing, documentation, and code-review practices.

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 maintain data infrastructure, ingestion pipelines, orchestration, transformations, and analytical data models. The role involves optimizing Redshift, Airflow, and dbt, decommissioning legacy systems, supporting AI-powered data capabilities, and collaborating with analysts, engineers, and product teams. It also includes applying data engineering best practices, translating business requirements into scalable solutions, and contributing to team knowledge sharing.

Build interactive data visualizations, maps, dashboards, and 3D experiences that turn complex energy data into clear narratives. Work independently with marketing, policy, press, leadership, and data science teams using React, Next.js, Mapbox, WebGL, and Blender. Own projects end to end, select appropriate visualization techniques, communicate insights effectively, and rapidly adapt to varied project requirements.

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, Python, Apex, and integration code; test security, performance, and edge cases; troubleshoot data and API issues; and support production deployment and stabilization. Collaborate with architects, customers, partners, and technical teams while documenting reusable implementations, runbooks, and platform feedback.

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.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Lead a small data engineering team as a hands-on manager to evolve the BigQuery warehouse, dbt transformations, ingestion and orchestration (Airflow, dlt, serverless CDC), and the semantic/BI layer (Tableau, Metabase, Lightdash). Own data governance, pipeline reliability, observability, and drive AI-assisted engineering and production AI data pipelines while partnering with product and stakeholders to deliver trusted self-service analytics.

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.

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 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 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.

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.

Develop scalable backend systems and high-performance data infrastructure using Python, AWS, databases, search technologies, and event-driven microservices. Responsibilities include writing maintainable code, optimizing distributed systems, improving product quality and security, collaborating with full-stack and machine learning engineers, delivering user-facing features, and prototyping new technologies.