Hybrid Data Engineer

at Leonardo

• Job title: Data Engineer
• Job type: Full Time
• Experience level: Junior, Mid level
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: Airflow, Apache, Informatica, Kafka, PySpark, Scala, Spark, SQL, Tableau, Vault
• Location requirements: Hybrid, Genova, Italy

About the role

• Data Engineer developing data pipelines and stream processing solutions for Leonardo in the Cyber & Security Solutions area. Supporting data ingestion, processing, and analytics for large-scale datasets.

Responsibilities

• Sviluppare data pipelines per ingestion, processing e transformation di grandi volumi di dati
• Implementare batch processing jobs con Apache Spark (PySpark, Scala)
• Sviluppare real-time data pipelines con Apache Kafka e Apache Flink
• Implementare stream processing applications per event transformation, enrichment e aggregation
• Orchestrare workflows complessi con Apache Airflow (DAG design, dependencies, scheduling)
• Sviluppare trasformazioni analitiche con SQL avanzato e dbt per analytics layers
• Sviluppare streaming aggregations con windowing operations (tumbling, sliding, session windows)
• Integrare stream processing con batch layers per unified analytics
• Implementare exactly-once processing semantics e state management in Flink
• Sviluppare Kafka consumers e producers con optimal configuration for throughput
• Implementare data quality testing e validation frameworks
• Integrare con data lakehouse (Delta Lake, Iceberg) e object storage per data persistence
• Implementare stream-to-lake integration per data persistence in lakehouse
• Sviluppare data modeling (dimensional, star schema) per analytics e reporting
• Collaborare con analytics teams per requirements gathering e data modeling
• Ottimizzare performance di Spark jobs, query execution plans e streaming applications per low-latency processing
• Implementare incremental processing patterns per efficiency
• Implementare monitoring e alerting per streaming pipelines health
• Gestire backpressure e failure recovery in streaming applications
• Supportare integration con BI tools (Tableau, PowerBI) per reporting
• Contribuire a DataOps practices (CI/CD for data pipelines, testing, monitoring) e best practices per stream processing

Requirements

• Laurea Magistrale in Ingegneria Informatica, Matematica, Statistica, Fisica, Informatica o equivalente
• 2 a 5 anni di esperienza nel ruolo, o più di 5 anni di esperienza in ruoli analoghi
• Data processing con Apache Spark (PySpark, Scala APIs) per batch workloads
• Stream processing con Apache Flink (DataStream API, Table API, SQL)
• Apache Kafka per event streaming (producers, consumers, Kafka Streams API, Kafka Connect)
• Real-time data pipelines con windowing operations e event-time processing
• State management e fault tolerance in streaming applications (checkpointing, savepoints)
• Orchestration con Apache Airflow (DAG design, custom operators, sensors, XComs)
• SQL avanzato (window functions, CTE, recursive queries, query optimization)
• dbt per analytics transformations e data modeling.
• Data modeling (dimensional modeling, star schema, data vault basics)
• Data lakehouse platforms (Delta Lake, Apache Iceberg) con ACID transactions
• Data quality frameworks (Great Expectations, dbt tests)
• Exactly-once semantics e watermarking for late data handling
• Performance tuning for low-latency and high-throughput.
• Conoscenze linguistiche: Italiano madrelingua, Inglese professionale (B2)

Benefits

• Disponibilità a brevi trasferte su territorio nazionale
• Certificazioni data engineering (Databricks, Snowflake), streaming (Confluent Certified Developer for Apache Kafka, Flink) sono titoli preferenziali