Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and build data pipelines for complex fixed income instruments.
- Company: Join a fast-growing tech company with a flexible remote culture.
- Benefits: Competitive salary, benefits, and the freedom to work remotely or in Edinburgh.
- Why this job: Make an impact in a unique role focused on fixed income data.
- Qualifications: Experience with fixed income products and strong data engineering skills.
- Other info: Dynamic environment with opportunities for growth and collaboration.
The predicted salary is between 36000 - 60000 £ per year.
This is a great opportunity to join a young, fast-growing business and to develop really innovative technology in an environment geared towards getting the best of out your engineering skills. You are free to work remotely, or visit their Edinburgh City Centre offices. Real flexibility over how, and where, you do your thing is a key pillar of their culture.
If you have real hands-on experience with fixed income products and enjoy working close to the data, this is a genuinely rare opportunity.
The RoleYou’ll be involved in:
- Designing and building data pipelines for complex fixed income instruments
- Ingesting, normalising, validating, and exposing market and reference data
- Working closely with domain experts to encode real-world fixed income logic
- Handling time-series, temporal, and event-driven financial datasets
- Indexing and querying large volumes of structured and unstructured data
- Contributing domain knowledge across products such as:
- Sovereign and sub-sovereign debt
- Corporate bonds and credit instruments
- Municipals and structured cashflows
This role suits someone who understands that bond data is fundamentally different from equities — and enjoys that challenge.
Technical EnvironmentThe engineering culture is pragmatic, modern, and data-focused. You’ll work with:
- Backend systems written in functional or functional-leaning languages (e.g. Elixir, Scala, F#, Haskell, Clojure, or similar)
- Relational databases and advanced SQL for complex financial datasets
- Infrastructure designed for simplicity, reliability, and performance
- Machine learning and LLM-adjacent workflows for document-heavy data
Exact tools matter less than strong engineering fundamentals and curiosity.
What They’re Looking For- Proven experience working with fixed income products and markets
- Strong understanding of bonds, pricing, yields, curves, and cashflows
- Experience building or maintaining data-heavy platforms
- Confidence working with messy, real-world financial data
- Ability to collaborate closely with analysts, product, and engineering peers
This is not a generic data role. Domain expertise in fixed income is essential.
If this excites you, apply now for immediate consideration!
Bright Purple is an equal opportunities employer. We are proud to work with clients who value diversity, inclusion, and fairness across the technology industry.
Fixed Income Data Engineer employer: Bright Purple
Contact Detail:
Bright Purple Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Fixed Income Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those who work with fixed income products. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio or GitHub showcasing your data engineering projects, make sure to share it during interviews. It’s a great way to demonstrate your hands-on experience with complex datasets.
✨Tip Number 3
Prepare for technical interviews by brushing up on your SQL and data pipeline design skills. Be ready to discuss how you’ve tackled messy financial data in the past – they’ll want to see your problem-solving chops!
✨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, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Fixed Income Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your hands-on experience with fixed income products. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about this opportunity and how your background makes you a perfect fit for our team. Keep it engaging and personal!
Showcase Your Technical Skills: Since we’re all about data, make sure to mention any experience you have with backend systems, SQL, or any functional programming languages. We love seeing candidates who are curious and eager to learn!
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 Bright Purple
✨Know Your Fixed Income Stuff
Make sure you brush up on your knowledge of fixed income products. Understand the nuances of bonds, pricing, yields, and cashflows. Being able to discuss these topics confidently will show that you’re not just a data engineer, but someone who truly understands the domain.
✨Show Off Your Data Pipeline Skills
Prepare to talk about your experience designing and building data pipelines. Have specific examples ready where you've ingested, normalised, or validated financial data. This is your chance to demonstrate your technical prowess and how it applies to complex datasets.
✨Get Familiar with Their Tech Stack
Research the programming languages and tools mentioned in the job description, like Elixir or Scala. Even if you haven’t used them extensively, showing that you’ve done your homework and are eager to learn can set you apart from other candidates.
✨Be Ready for Collaboration Questions
Since this role involves working closely with analysts and product teams, be prepared to discuss your collaboration experiences. Think of examples where you’ve successfully worked in a team to solve problems or deliver projects, especially in a data-heavy environment.
