At a Glance
- Tasks: Lead a global team to design and optimise high-performance data systems.
- Company: Dynamic tech firm focused on trading, analytics, and AI/ML solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Join a collaborative culture that values your contributions and career development.
- Why this job: Shape the future of data engineering in a fast-paced, innovative environment.
- Qualifications: 7-10 years in data engineering with strong Azure and Snowflake expertise.
The predicted salary is between 80000 - 100000 £ per year.
The Data Engineering Manager is responsible for defining and executing a modern, scalable, and governed data engineering strategy to support trading, analytics, and AI/ML use cases across the organization. This role sits within the core technology function, reporting to the Head of Technology, and works with platform, infrastructure, and security teams to build reliable, governed, high-performance data systems that serve the entire business. The focus is on delivering high-quality, reliable, and reusable data products through event-driven architectures and high-throughput data pipelines.
The role combines hands-on data engineering expertise with full ownership of the underlying data platform and its integration within the broader infrastructure landscape, ensuring alignment with enterprise architecture, security, and operational standards. Working with Trading, Risk, Finance, and Technology teams, this role ensures that complex data is transformed into trusted, accessible, and performant datasets aligned with governance frameworks and the firm’s semantic/knowledge-graph vision.
Responsibilities
- Define and execute the end-to-end data engineering strategy (ingest → govern → serve → observe), aligned with enterprise architecture, platform standards, and data governance.
- Own the design, build, and operation of the data platform on Azure and Snowflake, including integration with infrastructure components, ensuring scalability, security, and reliability.
- Design and implement event-driven architectures and high-throughput data pipelines to support real-time and batch data processing.
- Develop and industrialise high-performance data pipelines across key domains such as market/curve data, ETRM/CTRM systems, and finance/settlement, ensuring SLOs, lineage, and DR/BCP requirements are met.
- Define and enforce engineering standards across data pipelines, data models, APIs, and serving layers (SQL/API/Graph) to ensure consistency, reuse, and scalability.
- Embed data governance by design, including data contracts, data quality rules, access controls, masking, retention, and compliance requirements.
- Drive performance optimisation and cloud cost optimisation through efficient architecture and FinOps practices.
- Own the reliability and operational excellence of the data platform, including observability (metrics, logs, traces), incident management, and continuous improvement of critical data flows.
- Lead, coach, and scale a global team of data engineers, ensuring strong technical standards, delivery excellence, and a high-performing engineering culture.
- Act as the key interface between data engineering and the broader technology ecosystem (platform, infrastructure, security), ensuring seamless integration and alignment with enterprise standards.
- Partner with Business to prioritise initiatives and deliver impactful, production-grade data solutions.
Profile
- Bachelor’s degree or higher in Computer Science, Engineering, Applied Mathematics, or a related field.
- 7–10 years of experience in data engineering or data platform roles, including team leadership in complex, real-time environments.
- Strong hands-on experience with Azure as well as Snowflake, including performance, security, and cost optimisation.
- Solid experience with modern data architectures and tools (i.e. Terraform, Dagster).
- Proven ability to build and operate scalable, high-performance data pipelines with strong focus on reliability and observability.
- Strong understanding of platform and infrastructure concepts.
- Experience with data governance practices and enabling data for AI/ML use cases.
- Strong stakeholder management and communication skills, with the ability to work across technical and business teams.
- Ability to manage and prioritize multiple tasks in a fast-paced, deadline-driven environment.
- Energy commodity trading experience is a real advantage.
- Other cloud platform knowledge is a plus.
- Fluent in English (spoken and written).
If you think the open position you see is right for you, we encourage you to apply. Our people make all the difference in our success.
Data Engineer Manager employer: Gunvor UK Limited
As a Data Engineer Manager at our company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and excellence. We offer competitive benefits, including professional development opportunities and a commitment to employee growth, all within a cutting-edge technology environment located in a vibrant city. Join us to lead a global team in delivering impactful data solutions while enjoying the unique advantages of working in a forward-thinking organisation that values your contributions.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer Manager
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving Azure and Snowflake. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice common interview questions related to data engineering and be ready to discuss how you've tackled challenges in past roles.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications from passionate candidates who are eager to join our team. Make sure to highlight your experience with data governance and high-performance data pipelines.
We think you need these skills to ace Data Engineer Manager
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Data Engineering Manager role. Highlight your hands-on experience with Azure and Snowflake, and don’t forget to mention any leadership roles you've had in data engineering.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about data engineering and how your background aligns with our goals at StudySmarter. Be specific about your achievements and how they relate to the responsibilities outlined in the job description.
Showcase Your Projects:If you’ve worked on relevant projects, make sure to include them in your application. Whether it’s building high-performance data pipelines or implementing event-driven architectures, we want to see what you’ve accomplished and how it can benefit us.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining the StudySmarter team!
How to prepare for a job interview at Gunvor UK Limited
✨Know Your Data Inside Out
Make sure you’re well-versed in the specifics of data engineering, especially around Azure and Snowflake. Brush up on your knowledge of event-driven architectures and high-throughput data pipelines, as these will likely be hot topics during your interview.
✨Showcase Your Leadership Skills
As a Data Engineering Manager, you'll need to demonstrate your ability to lead and coach a team. Prepare examples of how you've successfully managed teams in the past, focusing on delivery excellence and fostering a high-performing culture.
✨Understand the Business Context
Familiarise yourself with the trading, risk, and finance domains relevant to the role. Being able to discuss how data engineering supports these areas will show that you understand the bigger picture and can align your strategies with business needs.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions about data governance, performance optimisation, and cloud cost management. Be ready to explain your thought process and decision-making when it comes to building scalable data solutions.
