Databricks Data Engineer
il y a 2 semaines
About the Company
A high growth technology business is looking for a Senior Databricks Data Engineer to help modernise and scale its core data platform.
About the Role
This is a key hire that will take ownership of Databricks across the organisation and play a central role in unifying more than forty data sources into a single, reliable environment that supports product analytics and wider business decision making.
Responsibilities
- Lead Databricks administration including performance, governance and environment management
- Build, optimise and maintain data pipelines in SQL and Python
- Industrialise existing workflows and help unify over forty data systems into one platform
- Implement and champion CI CD, Git integration and automation best practice
- Support internal teams with solution architecture, job optimisation and technical guidance
- Build proof of concept solutions to demonstrate the value of the platform and influence stakeholders
- Contribute to the development of new data products and AI driven initiatives
Qualifications
- Strong hands on experience with Databricks
- Excellent SQL and Python skills
- Background in modern data engineering with five plus years of experience
- Solid understanding of DevOps and DataOps practices including CI CD and Git
- Experience working with complex or fragmented data estates
- Ability to work closely with cross functional teams and communicate clearly with stakeholders
- Experience mentoring or managing junior engineers
Required Skills
- Strong hands on experience with Databricks
- Excellent SQL and Python skills
- Background in modern data engineering with five plus years of experience
- Solid understanding of DevOps and DataOps practices including CI CD and Git
- Experience working with complex or fragmented data estates
- Ability to work closely with cross functional teams and communicate clearly with stakeholders
- Experience mentoring or managing junior engineers
Opportunity to lead platform modernisation from the ground up. Significant autonomy and influence on data engineering standards. Work on high impact internal and product facing data initiatives. Hybrid working model with flexibility.
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