Data Engineer - Markel : Job Details

Location: London (Hybrid - 2 days onsite per week)Job Type: Full-time, PermanentSalary: £65,000 - £75,000 + Bonus + Benefits

Are you a Data Engineer with hands-on experience in PySpark and Databricks?Do you want to join a team where your work powers decision-making, advanced analytics, and machine learning models?

We're looking for a Data Engineer who is passionate about building scalable data solutions using modern cloud tools and frameworks - particularly Databricks, PySpark, and Python.

• Develop and optimise data pipelines using Databricks and PySpark

• Work on large-scale structured and unstructured data projects supporting AI/ML solutions

• Collaborate with data scientists, analysts, and software engineers across the business

• Design and deploy RESTful APIs for real-time and batch data integration

• Participate in code reviews and enforce best practices for performance and maintainability

• Manage and enhance CI/CD pipelines for seamless deployment of data workflows

• Write clean, testable, scalable code following modern software engineering principles (SOLID, DRY, etc.)

• Strong hands-on experience with Databricks and PySpark

• Proficient in Python for data engineering and automation tasks

• Experience designing and maintaining ETL/ELT pipelines in production

• Good understanding of cloud platforms (preferably Azure or AWS)

• Familiarity with data governance, data quality, and scalable architecture

• Experience working in agile teams and CI/CD environments

• Exposure to AI/ML platforms or data science collaboration is a bonus

• Work on forward-thinking data initiatives with tangible business impact

• Be part of a collaborative, empowered team driving innovation and performance

• Hybrid working model with flexibility and work-life balance

• Access to ongoing training, development, and career growth pathways

• Competitive salary, performance bonus, and full benefits package

Ready to apply?

If you're an experienced Data Engineer looking to take ownership of real data challenges using Databricks and PySpark, we'd love to hear from you.