Senior Databricks Data Engineer

Jobgether • United Kingdom

Remote

This Job is No Longer Active This position is no longer accepting applications

AI Summary

Design, build, and optimize enterprise-scale data pipelines on Azure Databricks. Implement data governance and security best practices. Collaborate with cross-functional teams to align platform solutions with enterprise standards.

Key Highlights

Design and optimize ETL/ELT data pipelines

Implement data governance and security best practices

Collaborate with cross-functional teams

Key Responsibilities

Design, develop, and optimize ETL/ELT data pipelines using Azure Databricks

Configure and maintain Databricks workspaces, clusters, jobs, repositories, and workflow schedules

Implement and enforce data governance and security best practices

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Technical Skills Required

Azure Databricks Python PySpark SQL Delta Lake Git Spark performance tuning Distributed computing concepts

Benefits & Perks

Competitive compensation

Fully remote work environment

Delivery of work equipment suited to the role and responsibilities

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Nice to Have

Familiarity with AI/ML lifecycle and MLflow model management

Experience working in Agile or DevOps-oriented teams

Job Description

In this role, you will design, build, and optimize enterprise-scale data pipelines on Azure Databricks, supporting structured, semi-structured, and unstructured data. You will work closely with data architects, security teams, and business stakeholders to implement best practices for data governance, security, and high-performance data processing. This position involves hands-on development of Delta Lake architectures, CI/CD pipelines, and orchestrated workflows to deliver reliable, scalable data products. Operating in a fully remote, collaborative environment, you will have the opportunity to influence the overall data platform strategy while solving complex technical challenges. Your work will enable analytics, reporting, and AI initiatives across the enterprise. The role combines technical depth, architectural insight, and operational excellence, offering strong growth potential in cloud data engineering.

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