Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Develop scalable Python applications and optimise data pipelines using Databricks and PySpark.
- Company: Join a Fortune 500 financial services firm leading in data and analytics transformation.
- Benefits: Enjoy a competitive salary, performance bonuses, flexible working, and comprehensive health coverage.
- Why this job: Be part of a dynamic team shaping the future of data science and AI in a global context.
- Qualifications: Experience in Python, Databricks, and PySpark; strong cloud platform knowledge required.
- Other info: Work in a hybrid environment with opportunities for career development and global project exposure.
The predicted salary is between 72000 - 108000 £ per year.
Company: Fortune 500 Financial Services firm
Location: Hybrid - London
Type: Permanent
Salary: £90k + 20% bonus + Exceptional Benefits
Are you a detail-oriented Python Developer who thrives in complex data environments? Do you have hands-on experience with Databricks, PySpark, and cloud-native data engineering? We’re hiring a Data Engineer on behalf of a Fortune 500 firm undergoing a major data and analytics transformation. You’ll join a global Advanced Analytics function working at the cutting edge of data science, engineering, and AI.
Why this role? You’ll join a cross-functional team that includes data scientists, actuaries, software engineers, and AI specialists - all working together to build intelligent data platforms that power business-critical decisions across the organisation. This is a cloud-first, engineering-led environment where you’ll play a key role in developing high-quality, scalable data products.
What You’ll Do:
- Build and maintain scalable Python applications using Databricks and PySpark
- Design and optimise robust data pipelines and processing frameworks
- Write clean, modular, and testable code aligned with SOLID principles
- Contribute to CI/CD pipelines, automated testing frameworks, and deployment tools
- Collaborate with stakeholders across data science, analytics, and engineering
- Build and expose RESTful APIs for secure data integration across platforms
- Help shape the data architecture that underpins machine learning and AI models
What You’ll Bring:
- Proven experience developing in Python for data-intensive applications
- Hands-on expertise with Databricks and PySpark
- Strong grasp of cloud data platforms and modern engineering practices
- Familiarity with CI/CD, version control (e.g. Git), and automated testing
- Ability to work effectively in cross-border, cross-functional teams
- A proactive, delivery-driven mindset with strong attention to detail
What’s in It for You:
- Excellent salary + performance bonus
- Private medical and dental cover, enhanced pension, life insurance, income protection
- Flexible working options, including holiday buy/sell and season ticket loans
- Training budget and career development pathways in a forward-thinking, data-driven organisation
- Exposure to global projects in a high-impact function
This opportunity is with a Fortune 500 company known for its long-term thinking, innovation, and technical excellence. With operations spanning over 20 countries, the firm is investing heavily in data and analytics to shape its future – and this role is crucial to that mission.
Data Engineer – Python | Databricks | PySpark employer: DATAHEAD
Contact Detail:
DATAHEAD Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer – Python | Databricks | PySpark
✨Tip Number 1
Familiarise yourself with Databricks and PySpark by working on personal projects or contributing to open-source initiatives. This hands-on experience will not only enhance your skills but also give you concrete examples to discuss during interviews.
✨Tip Number 2
Network with professionals in the data engineering field, especially those who work with Python and cloud technologies. Attend meetups or webinars to connect with potential colleagues and learn about the latest trends and challenges in the industry.
✨Tip Number 3
Prepare for technical interviews by practising coding challenges that focus on Python and data pipeline design. Websites like LeetCode or HackerRank can help you sharpen your problem-solving skills and get comfortable with the types of questions you might face.
✨Tip Number 4
Research the company’s recent projects and initiatives in data analytics and AI. Being knowledgeable about their goals and challenges will allow you to tailor your discussions and demonstrate how your skills can contribute to their success.
We think you need these skills to ace Data Engineer – Python | Databricks | PySpark
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, Databricks, and PySpark. Use specific examples of projects where you've built scalable applications or optimised data pipelines to demonstrate your expertise.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your skills align with their needs, particularly in developing high-quality data products and collaborating with cross-functional teams.
Showcase Relevant Projects: If you have any personal or professional projects that involved building RESTful APIs or working with CI/CD pipelines, include them in your application. This will provide concrete evidence of your capabilities.
Highlight Soft Skills: Don't forget to mention your soft skills, such as attention to detail and a proactive mindset. These are crucial in a collaborative environment and can set you apart from other candidates.
How to prepare for a job interview at DATAHEAD
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, Databricks, and PySpark in detail. Bring examples of projects where you've built scalable applications or optimised data pipelines, as this will demonstrate your hands-on expertise.
✨Understand the Company’s Data Strategy
Research the Fortune 500 firm’s approach to data and analytics transformation. Understanding their goals and how your role fits into their strategy will help you articulate how you can contribute effectively.
✨Prepare for Collaborative Scenarios
Since the role involves working with cross-functional teams, be ready to discuss your experience collaborating with data scientists, engineers, and other stakeholders. Share specific examples of how you’ve successfully worked in diverse teams.
✨Demonstrate a Proactive Mindset
Highlight instances where you took initiative in previous roles, whether it was improving processes or leading projects. This aligns with the company’s emphasis on a proactive, delivery-driven mindset.
