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- Tasks: Build and scale a private credit data platform, influencing investment decisions.
- Company: Dynamic investment firm focused on private markets and data innovation.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Join a developing Data & Analytics function with excellent career prospects.
- Why this job: Shape the future of data engineering and machine learning in finance.
- Qualifications: Degree in relevant field and strong programming skills in Python and SQL.
The predicted salary is between 60000 - 80000 £ per year.
We’re looking for a highly skilled Investment Data Engineer to help build and scale a private credit data and analytics platform. This is a rare opportunity to shape data infrastructure, analytics, and machine learning capabilities from the ground up—directly influencing investment decisions within a fast-growing private markets environment.
You will take ownership of the private credit data ecosystem end-to-end—designing robust backend infrastructure, building intuitive user-facing tools, and delivering advanced analytics that power deal sourcing and portfolio monitoring.
- Architect and build scalable data platforms using SQL Server and Azure
- Develop end-to-end data pipelines and front-end applications for business users
- Design and deploy machine learning models for text analysis, deal sourcing, and portfolio monitoring
- Expand and optimise data access across a growing portfolio of private credit deals
- Establish data governance frameworks and improve data quality processes
- Enable self-serve MI through tooling, training, and knowledge sharing
- Partner with internal teams and external vendors to deliver critical data solutions
Focused on private credit and direct lending strategies. A developing Data & Analytics function evolving into a centre of excellence for data engineering and machine learning.
Requirements:
- Degree in Computer Science, Data Science, Software Engineering, or similar
- Strong programming skills in Python, SQL, and C#/.NET or JavaScript
- Experience building production-grade data platforms in Azure (Data Lake, Databricks)
- Proven experience delivering ML models in production data pipelines
- Exposure to data governance and data quality frameworks
- Ability to work independently, drive projects, and operate in an evolving environment
- Experience with Alteryx, Microsoft Fabric, and Power BI
- Knowledge of Private Credit, Leveraged Finance, or Asset Management
- Work at the intersection of data engineering, machine learning, and finance
- Play a key role in shaping the future of data within the organisation
Senior Engineer, Data Engineering in London employer: Harrington Starr
Contact Detail:
Harrington Starr 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 on LinkedIn or at meetups. We can’t stress enough how important it is to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data platforms and machine learning. We love seeing real-world applications of your expertise.
✨Tip Number 3
Prepare for interviews by brushing up on technical questions related to SQL, Python, and Azure. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for talented individuals to join our team.
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 SQL Server, Azure, and any relevant machine learning projects. 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 tell us why you're passionate about data engineering and how you can contribute to our private credit data platform. Be genuine and let your personality come through.
Showcase Your Projects: If you've worked on any cool data projects, make sure to mention them! Whether it's building data pipelines or deploying ML models, we love seeing real-world applications of your skills. Include links if possible!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our team at StudySmarter!
How to prepare for a job interview at Harrington Starr
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like SQL Server, Azure, and Python. Brush up on your programming skills and be ready to discuss how you've used these tools in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in data engineering and how you overcame them. Use examples that highlight your ability to design robust backend infrastructure and develop end-to-end data pipelines.
✨Understand the Business Context
Familiarise yourself with private credit and direct lending strategies. Being able to connect your technical skills to real-world investment decisions will show that you understand the bigger picture and can contribute meaningfully.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s data governance frameworks and how they optimise data quality processes. This not only shows your interest but also demonstrates your proactive approach to understanding their operations.