14 set - Firenze
Team.Blue
OverviewAs a Senior Data Engineer in Central Data Platform, you build and govern a scalable data ecosystem that delivers a 360-degree view of customers across 60+ brands.
You will design high-value data products and data architectures that serve BI, operations, and AI initiatives.
This is a cross-brand role, enabling trustworthy data as the single source of truth.
You'll collaborate with Analytics, ML, and AI teams to drive measurable business value and transformative insights.
ResponsabilitàDesign and deliver scalable data products providing a unified source of truth across 60+ brands
Build and optimize ETL/ELT pipelines for large-scale structured and unstructured data
Lead secure, cost-efficient data architecture design and enforce best practices in modeling, orchestration, and observability
Implement data governance with lineage, metadata management, and quality controls for a reliable semantic layer
Collaborate with Analytics, ML, and AI teams to translate business needs into technical solutions
Tune data pipelines for peak performance focusing on indexing, query optimization, and schema evolution
Mentor junior engineers and foster a culture of technical excellence
Requisiti fondamentali7+ years in data engineering or data management
Advanced degree (Masters or PhD) in Computer Science, STEM, or related quantitative field
Proven track record deploying high-performance data solutions with measurable business value
Deep hands-on experience with Databricks (PySpark, Delta Lake, Unity Catalog) or equivalent table formats
Expert-level SQL with optimization and data modeling skills (Dimensional, Star Schema, Snowflake)
Proficiency in at least one cloud provider (AWS, Azure) and modern orchestration tools (Airflow, dbt)
Strong DevOps/Engineering skills (Docker, Kubernetes) and CI/CD with GitLab or GitHub
Experience with data versioning, schema evolution, and distributed metadata management
Strategic thinking
Effective communication with non-technical stakeholders
Problem-solving under fast-paced, multi-workstream environments
Databricks (PySpark, Delta Lake, Unity Catalog)
Advanced SQL and data modeling (Dimensional, Star Schema, Snowflake)
Cloud platforms (AWS, Azure)
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