Data Engineering Manager

2 months ago


Århus, Århus Kommune, Denmark Databricks Inc. Full time
About the Role

We are seeking a talented Engineering Manager to lead our Platform team at Databricks, driving the development of our cloud infrastructure and data processing platform.

This is an exceptional opportunity to build and manage a high-performing team that will be responsible for delivering scalable and reliable data processing solutions to our customers.

You will work closely with our engineering and product leadership teams to define the technical direction of our platform and drive cross-functional collaboration to achieve our goals.

Key Responsibilities:

  • Lead a team of engineers to design, develop, and deploy high-performance data processing solutions.
  • Collaborate with engineering and product leadership to define technical vision and roadmaps.
  • Develop and implement processes to ensure high-quality, scalable, and reliable software systems.
  • Work closely with cross-functional teams to ensure seamless integration and deployment of our platform.
  • Grow and develop a high-performing team of engineers with strong technical skills and leadership capabilities.
Requirements
  • 5+ years of experience in software engineering, with a focus on cloud infrastructure and data processing.
  • Experience leading teams of engineers to deliver high-quality software solutions.
  • Demonstrated expertise in designing and developing scalable, reliable, and high-performance software systems.
  • Strong technical skills in cloud infrastructure, data processing, and software development.
  • Excellent communication and leadership skills, with the ability to collaborate with cross-functional teams.

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn, and Facebook.



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