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Databricks Expert

Pubblicato il 17-08-2026 - Ntt Data Europe & Latam in Torino

The Databricks Expert in NTT Data knows the challenges of a cloud native environment and is able to think outside the box finding innovative, yet simple, solutions to everyday issues.

We are looking for an engineer/architect who can be the bridge between Data Science and Data Engineering, having a clear understanding of both worlds.

If you are a Data Scientist looking for more experience in preparing data, or a Data Engineer with interest also in Data Science, willing to have a KAIZEN approach, continuously improving technical skills and knowledge, you can be a Databricks Expert in NTT Data’s Data&Intelligence; Service Line, in an environment where Japanese and European cultural elements are intertwined.

Your Contribution In This Role

In this role you will support customers in Big‑Data projects, working in partnership with business analysts and solution architects to understand use cases, data needs, and outcome objectives. You will deliver data acquisition, transformations, and improvement, cleansing, conversion, compression,



and loading of data into data lakes for consumption by analytics and machine learning models.

The ideal profile
- Working experience in a cloud native environment in at least one of the 3 major public clouds (GCP/AWS/Azure), 5 years experience on Databricks is preferred
- Experience and knowledge of Big Data Architectures, cloud and on‑premise
- Azure infrastructure and networking working experience
- Experience in building and delivering proofs‑of‑concept to address specific business needs using the most appropriate techniques, data sources and technologies
- Experience in migrating workloads from on‑premise to cloud environment
- Experience in monitoring distributed infrastructure, using Azure tools or open source ones (e.g., Grafana, Prometheus)
- Proven experience in Java, Scala, Python, and shell scripting
- SQL language knowledge
- Experience in leading medium teams
- Working experience with Apache Spark, Databricks, Azure Data Facto

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