Data Architect

• The data architect contributes to the phases of the elaboration of a “Project Solution Document”:

• Reviewing business & project chapters and writing applicative, technical and infrastructure chapters
• Acting from Investmentcommitteepreparation including project defense in local & central IT project committees

• Support business and IT teams in the definition of the data strategy of the subsidiaryor the specific strategy for a product, by mean of conducting assessments, comparingsolutionsand presenting them to a broad audience (IT,business, top management, ...)
• Co-build new datacomponent

• Support the setup andthe deploymentof a new data product within an existing entity squad (Event, Data Preparation, Big Data platform).
• Interact with local/central IT and production teams under the responsibility of IT tech leador product manager

• The main responsibilities are covering functional and technical architecture layers, involving data

capabilities (ingestion, processing, storage, exchange, management, access)

• Define the vision of data product and interactions with IS
• Technology watch and advising
• Design functional and technical architecture to align with businessrequirementand value definition
• Support DevOps and IT delivery teams in the implementation

Contributing Responsibilities:

• Team player 
• Adhere to the standards and practices followed in the Project
• Foster a culture of continuous learning and improvement within the team.
• Collaborate with cross-functional teams to understand data requirements and deliver solutions.

Technical & Behavioral Competencies

Technical Skills

• 10+ years of hands-on data engineering experience, with a strong focus on scalable, high-performance data systems.
• Expertisein distributed data processing frameworks (e.g., Spark Compute, Apache Airflow) and modern storage formats (Parquet/Iceberg, ORC).
• Proficiencyin Spark (Scala/Python/Java), Kafka, and Elasticsearch for real-time and batch data pipelines.
• Deep knowledge of relational (RDBMS) and NoSQL databases, with optimization experience for large-scale workloads.
• Hands-on experience with object storage (AWS S3, IBM COS) and data virtualization tools (e.g.,Dremio).

• Strong Airflow orchestration experience, including DAG design, scheduling, and monitoring.
• Familiarity with CI/CD pipelines (GitLab, Jenkins, or similar) for data workflow automation.
• Experience integrating Parquet/ORC formats with cloud/on-prem storage systems
  • Nice to have: Working knowledge of HDFS, Hadoop and Hive