Hybrid Cloud Data Architect – Rome

Bridgestone; Roma; Hybrid; EUR 70,000 - 95,000; Full time

Benefits offered by this job

Onboarding program

Career growth

CSR activities

Global work environment

Work-life balance

Diversity & inclusion

Job summary

Bridgestone is seeking a Digital Solution Architect in ROMA, IT, to lead data engineering initiatives within the Digital Lab. You will design and deploy modern data pipelines, integrate external data sources, and enable advanced analytics with a cloud-first approach.

The role requires strong expertise in Azure data services, Spark, and data modeling, with hands-on experience in batch and streaming processing.

Qualifications

• Master’s Degree in Computer Science or equivalent.
• Minimum 3 years of experience with Big Data projects (HDFS, Hadoop, Spark, Spark Streaming, Hive, HBase, etc.).
• Minimum 3 years building Batch and Streaming data pipelines in Microsoft Azure using Spark.

Responsibilities

• Design, develop and deliver reliable modern data pipelines for data projects.
• Implement batch and streaming data ingestion, processing and transformations.
• Provide access to final datasets to Data Science team for analytics.
• Manage data and metadata in Big Data Warehouse and data stores.
• Design and maintain Data Lake for Big Data analytics.
• Deploy, configure and monitor Cloud Data Platform infrastructure.

Skills

Big Data architecture

Data modeling

SQL

Spark

Cloud architecture

Education

Master’s Degree in Computer Science

Tools

Azure Data Factory

Azure Databricks

Azure Data Lake Store

Azure SQL Database

Azure HDInsight

Azure Functions

AWS Lambda

Azure App Service

Azure CLI / ARM Templates

Job description

Bridgestone is seeking a Digital Solution Architect in ROMA, IT, to lead data engineering initiatives within the Digital Lab. You will design and deploy modern data pipelines, integrate external data sources, and enable advanced analytics with a cloud-first approach.

The role requires strong expertise in Azure data services, Spark, and data modeling, with hands-on experience in batch and streaming processing.