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AWS - Data Architect

Pubblicato il 04-09-2026 - Ntt Data Italia in Torino

Questa posizione è in NTT Data Italia

Il processo di selezione sarà interamente gestito NTT Data Italia.

Questa possibilità è disponibile in Bari - Italy, Bologna - Italy, Cosenza - Italy, Milano - Italy, Napoli - Italy, Pisa - Italy, Roma - Italy, Salerno - Italy, Torino - Italy, Treviso - Italy.

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Your role as an AWS Data Architect in NTT Data will be supporting customers in AWS Data Platform projects, working in partnership with business analysts and solution architects to understand use cases, data needs, and outcome objectives. We are looking for an engineer that can be the bridge between Data Science and Data Engineering, having a clear understanding of both worlds. Your contribution in this role:
- Create and maintain optimal data pipeline architecture by designing and implementing data ingestion solutions on AWS using AWS native services (such as GLUE, Lambda) or using data management technologies (such as Talend or Informatica)
- Design and optimize data models on AWS Cloud using AWS data stores such as Redshift, DynamoDB, RDS, S3




- Design operations architecture and conduct performance engineering for large scale data lakes in production environment
- Participate in client design workshops and provide tradeoffs and recommendations towards building solutions.
- Mentor other engineers in coding best practices and problem solving

The ideal profile
- Working experience in a cloud native environment in one of the 3 major public clouds (GCP, AWS, Azure), at least 5 year experience on AWS is preferred
- Experience and knowledge of Big Data Architectures, cloud and on premise
- AWS infrastructure and networking working experience
- AWS Collection Services: Kinesis, Kafka, Database Migration Service
- AWS main Storage Service: S3, RDS, Redshift, DynamoDB
- AWS main Compute Service: EC2, Lambda, ECS, EKS
- Experience in building and deliver proofs-of-concept, in order to address specific business needs, using the most appropriate techniques, data sources and te

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