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- Tasks: Enhance data pipelines and support analytics infrastructure in a fast-paced fintech environment.
- Company: Dynamic fintech company focused on innovation and growth.
- Benefits: Competitive salary, hands-on experience, and opportunities for rapid career development.
- Other info: Flat culture with a focus on trust, curiosity, and personal growth.
- Why this job: Join a growing team and make a real impact in the world of finance and data.
- Qualifications: 2-3 years in data analysis or engineering, strong Python and SQL skills.
The predicted salary is between 40000 - 48000 £ per year.
I’m supporting a growing fintech business on the search for a Data Analyst / Data Engineer to join its London quant and technology team. This is a strong opportunity for someone with around 2–3 years’ commercial experience who wants to work at the intersection of data, analytics, engineering and financial products. The role sits in a smart, fast-moving environment where the work is hands‑on and visible, and where the right person can develop quickly. The business is building out its London team following recent investment and is looking for people who are technically strong, curious and motivated by growth.
This role will suit someone who enjoys getting into the detail of data, improving how things work, and contributing across both technical and operational workflows. The role will focus on:
- Enhancing market data and security master pipelines across multiple providers and data structures
- Supporting the design and development of data microservices and analytics infrastructure
- Improving data quality, validation and control processes
- Helping maintain tools that support systematic investment and risk infrastructure
- Applying analytical thinking and, where relevant, machine learning techniques to data quality challenges
- Working closely with senior stakeholders across data and risk
The role would suit someone with:
- Around 2–3 years’ experience in data analysis, data engineering or a related role
- Strong Python and SQL skills
- Experience working with production data pipelines, transformations or structured datasets
- Strong attention to detail and a naturally investigative mindset
- Curiosity around systems, modern tooling and how data products are built
- Some interest in finance, markets, statistics or investment data would be useful, but deep finance experience is not essential
Additional fit points:
- More likely to suit someone from a startup, scale‑up or midsize environment than a very heavy corporate background
- They like people who are genuinely interested in technology outside of work too — side projects, tinkering or self‑driven learning all help
- The culture is flat, high‑trust and growth‑oriented
- London‑based role with an expectation of 4 days per week in the office
This is a very good move for someone who wants breadth, responsibility and the chance to grow in a business where there is still plenty to build.
Data Engineer in London employer: Goodman Masson
Contact Detail:
Goodman Masson Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the fintech space, especially those who work at companies you're interested in. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your data projects. This is your chance to demonstrate your Python and SQL prowess, and it’ll make you stand out when you apply through our website.
✨Tip Number 3
Prepare for interviews by brushing up on your analytical thinking and problem-solving skills. Be ready to discuss how you've tackled data quality challenges in the past—real examples will impress potential employers!
✨Tip Number 4
Stay curious! Keep learning about new tools and technologies in data engineering. Mentioning your side projects or self-driven learning during interviews shows you're genuinely passionate about the field.
We think you need these skills to ace Data Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experience mentioned in the job description. Highlight your 2-3 years of experience in data analysis or engineering, and don’t forget to showcase your Python and SQL skills!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you’re excited about this role and how your background aligns with our needs. Share any relevant projects or experiences that demonstrate your curiosity and investigative mindset.
Show Off Your Side Projects: If you’ve got any side projects or tinkering with technology outside of work, mention them! We love seeing candidates who are passionate about tech and self-driven learning, as it shows your genuine interest in the field.
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 this exciting opportunity in our growing fintech team!
How to prepare for a job interview at Goodman Masson
✨Know Your Data Inside Out
Make sure you brush up on your data analysis and engineering skills, especially in Python and SQL. Be ready to discuss specific projects where you've enhanced data pipelines or improved data quality. This will show your hands-on experience and technical strength.
✨Show Your Curiosity
This role values curiosity, so come prepared with questions about the company's data systems and tools. Share any side projects or tinkering you've done with data outside of work. It’ll demonstrate your genuine interest in technology and growth.
✨Understand the Financial Context
While deep finance experience isn't essential, having a basic understanding of financial products and markets can set you apart. Brush up on key concepts and be ready to discuss how data plays a role in investment and risk management.
✨Emphasise Team Collaboration
Since the role involves working closely with senior stakeholders, highlight your teamwork skills. Prepare examples of how you've collaborated in past roles, particularly in fast-paced environments. This will show that you're not just technically strong but also a great fit for their flat, high-trust culture.