At a Glance
- Tasks: Design and build innovative data pipelines for AI-driven trading solutions.
- Company: Join a pioneering team in financial services transforming AI and cloud technology.
- Benefits: Competitive salary, creative freedom, and direct impact on trading strategies.
- Other info: Be part of a small, high-impact team with excellent career growth opportunities.
- Why this job: Shape the future of trading with cutting-edge technology and collaborative culture.
- Qualifications: 5-10 years in data engineering, strong Python skills, and AWS experience.
The predicted salary is between 36000 - 60000 £ per year.
Join a pioneering team that’s redefining how AI and cloud-native platforms power front-office trading. We’re hiring a hands-on Data Engineer to help shape the future of our platform, a critical infrastructure that supports Quantitative and Strat developers in building next-generation trading solutions.
This is not a standard engineering role. You’ll work closely with front‑office traders and quantitative developers, focusing on the data engineering required to design, build, and operate bespoke generative AI and agent‑based systems used directly in trading workflows. The work you do will have a measurable impact on how strategies are developed, tested, and executed. If you’re motivated by building novel, production‑grade systems at the leading edge of technology, this role gives you the scope to do exactly that.
What You’ll Do
- Design, build, and maintain robust data pipelines for batch and streaming workloads, ensuring high data quality, reliability, and observability across cloud and on‑prem platforms.
- Model, store, and serve large‑scale datasets optimised for analytics, machine learning, and low‑latency consumption by AI‑driven trading systems.
- Build and optimise real‑time and near‑real-time data pipelines using Databricks and streaming technologies to ingest, process, and serve high‑volume market and trading data at scale.
- Design and implement secure, cost-aware, scalable systems using AWS services and Kubernetes.
- Contribute to best practices for agent‑based system infrastructure and mentor junior engineers when needed.
- Work across organisational boundaries and champion modern engineering trends.
- Stay ahead of the curve in agent‑based systems, AI infrastructure, and cloud-native tooling.
- Architect and develop cutting-edge platform services for AI-driven trading.
Tech Stack
- Programming: Python
- AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and Databricks
- On-Prem: Managed Kubernetes Platform and Hadoop ecosystem.
What we are looking for
- 5 – 10 years of experience in data engineering, ideally in platform or infra roles.
- Strong programming skills in Python; passion for code quality and testing.
- Experience with Databricks or similar tools.
- Experience with AWS services (S3, Glue, Kinesis, Lambda, ECS, IAM, KMS, API Gateway, Step Functions, MSK, CloudFormation).
- Experience working in a fast-paced environment in either engineering or analytical roles.
- Passion for being hands-on and contributing to a collaborative engineering culture.
Direct Impact: Be part of a team building agent‑based systems that traders and quants use daily to optimise strategies.
Creative Freedom: Open collaboration and the chance to bring your ideas to life.
Visibility: Be a big player in a small, high-impact team with exposure across the organisation.
Nice to Have
- Experience with on-prem Hadoop and Kubernetes.
- Familiarity with AWS cost management and optimisation tools.
- Knowledge of front-office developer workflows in financial services.
AI Data Engineer - Financial Services in Edinburgh employer: Caspian One
Join a forward-thinking company that values innovation and collaboration, where as an AI Data Engineer in Financial Services, you will play a pivotal role in shaping cutting-edge trading solutions. With a strong emphasis on employee growth, you will have the opportunity to work closely with front-office traders and quantitative developers, contributing to impactful projects while enjoying a dynamic work culture that encourages creativity and hands-on involvement. Located in a vibrant financial hub, this role offers unique advantages such as exposure to the latest technologies and the chance to make a significant difference in the fast-paced world of trading.
StudySmarter Expert Advice🤫
We think this is how you could land AI Data Engineer - Financial Services in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working in financial services or data engineering. Attend meetups, webinars, or even just grab a coffee with someone who’s in the know. You never know where a casual chat might lead!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, AWS, or Databricks. This is your chance to demonstrate your hands-on experience and passion for building innovative systems. Make sure to highlight any real-time data pipelines you've worked on!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with cloud-native platforms and how you’ve tackled challenges in data engineering. Practice common interview questions and think about how your work has made an impact.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our pioneering team. Let’s shape the future of AI and trading together!
We think you need these skills to ace AI Data Engineer - Financial Services in Edinburgh
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the AI Data Engineer role. Highlight your data engineering experience, especially with Python and AWS services, to show us you’re the right fit for our pioneering team.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you’re passionate about building innovative systems in financial services. Share specific examples of your past work that demonstrate your hands-on approach and collaborative spirit.
Showcase Your Projects:If you've worked on relevant projects, whether in a professional or personal capacity, make sure to mention them. We love seeing how you've applied your skills in real-world scenarios, especially with data pipelines and cloud technologies.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Caspian One
✨Know Your Tech Stack
Make sure you’re well-versed in the tech stack mentioned in the job description. Brush up on your Python skills and get familiar with AWS services like S3, Kinesis, and Databricks. Being able to discuss how you've used these tools in past projects will show that you're not just a fit for the role, but also passionate about the technology.
✨Understand the Business Context
Since this role is closely tied to front-office trading, take some time to understand how AI and data engineering impact trading strategies. Familiarise yourself with agent-based systems and how they are used in financial services. This knowledge will help you answer questions more effectively and demonstrate your genuine interest in the role.
✨Prepare for Hands-On Questions
Expect technical questions that require you to think on your feet. Be ready to discuss how you would design and build data pipelines or optimise real-time data processing. Practising coding problems related to data engineering can give you the edge you need to impress your interviewers.
✨Show Your Collaborative Spirit
This role emphasises collaboration, so be prepared to talk about your experiences working in teams. Share examples of how you’ve mentored junior engineers or contributed to a collaborative culture. Highlighting your ability to work across organisational boundaries will resonate well with the interviewers.
