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Databricks Hire, Train, Deploy

Build the data engineering talent behind your Databricks strategy

Develop Databricks Data Engineers with the technical expertise to create trusted data foundations for analytics, machine learning and AI.

Turn your lakehouse into a platform for data and AI

Databricks brings data engineering, analytics, machine learning and AI together on a unified platform. But realizing that potential depends on having Data Engineers who can build reliable pipelines, manage complex data workloads and create trusted foundations for downstream use.

As organizations move beyond experimentation and look to operationalize AI, the quality, accessibility and performance of their data becomes even more important.

Revolent’s Databricks Talent Program helps you create Data Engineers with the technical knowledge and practical experience to support those priorities from data ingestion and transformation through to analytics, machine learning and AI workloads.

What does a Databricks Data Engineer do?

Databricks Data Engineers help customers design and build reliable, scalable data foundations across the lakehouse. They develop the pipelines, transformations and data models that make information easier to trust, access and use while supporting the performance required for increasingly complex analytics, machine learning and AI workloads.

Their work helps organizations move data efficiently through the platform, improve quality and governance and ensure downstream teams have the dependable datasets they need to generate insight and build AI solutions.

Build and optimize scalable data pipelines and workflows in Databricks
Improve data quality, accessibility and performance across the lakehouse
Develop and manage transformations and models for analytics and downstream applications
Prepare trusted data for analytics, machine learning and generative AI workloads

Databricks training built around modern data engineering

Our Databricks Hire, Train, Deploy pathway combines structured technical learning with extensive practical application. As a Databricks Consulting Partner, our program is designed to prepare Data Engineers for the tools, workflows and responsibilities they are likely to encounter within production data environments.

Core areas can include:

An intensive 10-week training bootcamp focused on developing deployment-ready Databricks Data Engineers
More than 50% of training time dedicated to practical application and use-case-driven labs
Databricks learning pathways including Lakehouse Fundamentals, Apache Spark Developer and Data Engineer Associate
Training across Gitflow, advanced SQL, Airbyte, Fivetran and Tableau
Technical modules covering data modeling, scripting, visualization and data engineering patterns
Foundations in machine learning and generative AI-ready data practices
Continued learning after deployment, with the opportunity to deepen expertise in advanced data engineering, machine learning or generative AI

How we build your Databricks team

1 Mieten Sie

We identify professionals with relevant technical and industry experience who have the foundations needed to develop into effective Databricks Data Engineers.

2

Zug

Each professional completes intensive Databricks training covering core data engineering concepts, platform technologies and the wider tooling needed to work effectively within modern data environments.

3

Deploy

Once training is complete, they join your team for up to two years and apply their Databricks expertise directly to pipelines, lakehouse architecture and data workloads.

4

Entwickeln Sie

Learning continues throughout the deployment, allowing Data Engineers to deepen their technical expertise and specialize in areas such as advanced data engineering, machine learning or generative AI.

5

Konvertieren

At the end of the deployment, they can transition permanently into your organization, helping you retain the Databricks knowledge and experience developed within your environment.

Build the data foundations your AI ambitions depend on

As Databricks becomes more central to data and AI strategies, the demands placed on Data Engineering teams continue to expand. It is no longer only about moving data from one system to another. Teams need to create trusted, scalable data foundations that can serve analytics, machine learning and increasingly complex AI workloads.

Revolent’s Hire, Train, Deploy model gives you a way to build that expertise around your own Databricks environment, while developing people who can grow with your platform and changing technical priorities.

Ready to expand your Databricks team?

Tell us where you need more Data Engineering capacity and we’ll help you build Databricks talent aligned to your data and AI roadmap.