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- Tasks: Build and maintain data structures for investment monitoring and reporting.
- Company: Dynamic financial services organisation backed by private equity.
- Benefits: Competitive salary, benefits, delivery bonus, and hybrid working.
- Other info: Collaborative environment with opportunities to work on cutting-edge data platforms.
- Why this job: Join a growing team and make an impact in investment data engineering.
- Qualifications: 3-5 years in data engineering with strong Python 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
Ingegnere dei dati (f/m) in London employer: Dartmouth Partners
Contact Detail:
Dartmouth Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Ingegnere dei dati (f/m) in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the investment data field on LinkedIn or at industry events. We can’t stress enough how personal connections can lead to job opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects, data pipelines, and any analytics work you've done. This gives potential employers a taste of what you can bring to the table, especially in a role focused on data engineering.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and understanding of financial markets. We recommend practising common data engineering interview questions and being ready to discuss how you’ve solved real-world problems with data.
✨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 Ingegnere dei dati (f/m) in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Investment Data Engineer role. Highlight your experience with Python, data pipelines, and any relevant financial market knowledge. We want to see how your skills match 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 in the financial sector. Share specific examples of how you've tackled similar challenges in the past, and let us know why you want to join our team.
Showcase Your Technical Skills: Don’t forget to mention your technical skills! If you've worked with Snowflake, Databricks, or have experience in automating reporting with tools like Power BI, make sure these stand out. We love seeing candidates who can hit the ground running!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your documents in one go. Plus, it helps us keep track of your application better!
How to prepare for a job interview at Dartmouth Partners
✨Know Your Data Inside Out
Make sure you’re well-versed in the data structures and analytics relevant to investment monitoring. Brush up on your Python skills and be ready to discuss how you've used them in production environments. This will show that you can hit the ground running!
✨Understand the Business Context
Familiarise yourself with the financial services sector, especially around asset management and capital efficiency. Being able to speak knowledgeably about how data impacts decision-making in this context will impress your interviewers.
✨Showcase Your Collaboration Skills
Since the role involves partnering with investment professionals and risk stakeholders, prepare examples of how you’ve successfully collaborated in past roles. Highlight your ability to translate business needs into technical solutions—this is key!
✨Prepare for Technical Questions
Expect to dive deep into your experience with databases, data pipelines, and modern data platforms like Snowflake or Databricks. Be ready to discuss specific projects where you’ve implemented these technologies, as well as any AI tooling you've used.