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- Tasks: Build and maintain data structures for investment monitoring and reporting.
- Company: PE-backed financial services firm focused on innovation and growth.
- Benefits: Competitive salary, delivery bonus, hybrid working, and professional development opportunities.
- Other info: Collaborative environment with exposure to cutting-edge data platforms.
- Why this job: Join a dynamic team and make an impact in the world of finance and data.
- Qualifications: 3-5 years in data engineering with strong Python and database skills.
The predicted salary is between 75000 - 85000 £ per year.
Location: London (Hybrid working)
Salary: £75,000 - £85,000 + benefits + Delivery Bonus
We are supporting a PE‑backed financial services organisation with a sizeable balance sheet and a growing institutional investment platform. The business operates in regulated financial services, with a strong focus on long‑term asset management and capital efficiency.
Backed by private equity, the organisation is in a build‑out phase of its central investment capabilities, investing heavily in data, analytics and automation to support portfolio oversight, risk management and decision‑making.
The role sits within a central investment function based in London and works closely with senior stakeholders across investments, risk and finance. The business is hiring an Investment Data Engineer (Senior Associate) into its central investment office. This role is responsible for building and maintaining the data structures, analytics and workflows that support investment monitoring, reporting and portfolio analysis. You will partner directly with investment professionals and risk stakeholders, translating business needs into robust technical solutions.
You will work alongside a peer data developer, with shared ownership for the investment data stack and analytics toolkit.
- Design and maintain data architecture for structured and unstructured investment data
- Define, source and automate data feeds including market data, portfolio data and reporting outputs
- Develop production‑quality Python analytics for portfolio analysis, monitoring and reporting
- Deliver automated reporting and insight via Power BI or comparable analytics tools
- Partner with investment, risk and finance teams to translate requirements into scalable data solutions
3–5 years’ experience in a data engineering, analytics engineering or investment data role
- Strong Python skills used in a disciplined, production environment
- Solid experience working with databases and data pipelines
- Exposure to modern data platforms such as Snowflake or Databricks, including use of AI tooling across structured and unstructured data
- Working knowledge of financial markets
Senior Engineer, Data Engineering in London employer: Dartmouth Partners
Contact Detail:
Dartmouth Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Engineer, Data Engineering in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working in investment data roles. A friendly chat can lead to insider info about job openings or even a referral.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects and data engineering work. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and understanding of financial markets. Be ready to discuss how you've tackled challenges in past roles and how you can add value to their team.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive and engaged with our platform.
We think you need these skills to ace Senior Engineer, Data Engineering in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Investment Data Engineer. Highlight your experience with Python, data pipelines, and any relevant financial market knowledge. We want to see how your skills align with what we're looking for!
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 you can contribute to our investment team. Be sure to mention any specific projects or achievements that showcase your expertise.
Showcase Your Technical Skills: Since this role involves building data structures and analytics, don’t shy away from detailing your technical skills. Mention your experience with tools like Snowflake or Databricks, and any AI tooling you've used. We love seeing practical examples!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Dartmouth Partners
✨Know Your Data Inside Out
Make sure you’re well-versed in the data engineering concepts relevant to the role. Brush up on your Python skills and be ready to discuss how you've built and maintained data structures in previous roles. Being able to talk about specific projects where you’ve automated data feeds or developed analytics will really impress.
✨Understand the Business Context
Since this role involves working closely with investment professionals, it’s crucial to understand the financial services landscape. Familiarise yourself with key terms and concepts related to asset management and risk management. This will help you translate business needs into technical solutions during the interview.
✨Showcase Your Collaboration Skills
This position requires partnering with various teams, so be prepared to share examples of how you’ve successfully collaborated in the past. Highlight any experiences where you’ve worked with stakeholders from different departments, especially in finance or risk, to deliver data solutions that meet their needs.
✨Prepare for Technical Questions
Expect to face technical questions that assess your knowledge of databases, data pipelines, and modern data platforms like Snowflake or Databricks. Practise explaining your thought process when solving data-related problems, and be ready to demonstrate your coding skills if asked. A hands-on approach can set you apart!