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- Tasks: Transform data into client-ready insights and support RFP and DDQ responses.
- Company: Leading global asset manager with a focus on innovation.
- Benefits: Gain exposure to institutional processes and make an immediate impact.
- Other info: Join a high-performing team and contribute to revenue-generating activities.
- Why this job: Work at the intersection of data, product, and distribution in a dynamic environment.
- Qualifications: Experience with Python, SQL, and data warehousing; strong analytical skills.
The predicted salary is between 50000 - 60000 £ per year.
A leading global asset manager is seeking a Distribution Data Analyst to join its Client Group function in London. This is not a traditional data role. We’re looking for a commercially minded analyst who can translate data into client-ready insight, supporting high-quality RFP and DDQ responses that directly contribute to winning and retaining institutional business.
The Opportunity
You’ll sit within a high-performing RFP team, acting as the bridge between data and distribution. Rather than focusing purely on backend engineering, this role is about owning how data is used—ensuring it is accurate, accessible, and meaningful in a client-facing context.
What You’ll Be Doing
- Partner with RFP and product teams to deliver quantitative data for RFPs and DDQs
- Translate complex datasets into clear, accurate, client-ready outputs
- Improve how data is sourced, structured, and utilised across workflows
- Collaborate with Technology teams to enhance data access, automation, and reporting
- Identify and resolve data quality issues, ensuring consistency and reliability
- Support the development of a scalable data environment aligned to business needs
- Enable non-technical stakeholders to effectively access and use data
What We’re Looking For
This role requires a blend of technical capability and real-world application.
- Strong hands-on experience with Python, SQL, and data warehousing (e.g. Snowflake or similar)
- Proven ability to use data in a business-facing context, not just build pipelines
- Experience delivering tangible outputs (e.g. reports, analysis, client materials)
Highly Valued Experience
- Exposure to asset management or financial services
- Understanding of RFP/DDQ processes or client reporting
- Experience working with front-office or product teams
- Familiarity with BI tools (e.g. Tableau, Power BI)
What Sets Strong Candidates Apart
- A front-end mindset – comfortable engaging directly with stakeholders
- Evidence of impact and ownership, not just tools used
- Understanding of how data supports client acquisition and retention
- Ability to translate technical detail into commercial value
Why Apply?
- Work at the intersection of data, product, and distribution
- Gain exposure to institutional client processes
- Play a visible role in revenue-generating activity
- Opportunity to make an immediate impact within a globally recognised investment platform
Distribution Data Analyst - Fund Management in London employer: Ramsey Portia
Contact Detail:
Ramsey Portia Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Distribution Data Analyst - Fund Management in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working in asset management or data roles. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Prepare a portfolio or case studies that highlight how you've used data to drive business decisions. This will help you stand out when discussing your experience with potential employers.
✨Tip Number 3
Practice makes perfect! Get ready for interviews by rehearsing answers to common questions, especially around data usage in client-facing contexts. We want you to feel confident and ready to impress!
✨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 Distribution Data Analyst - Fund Management in London
Some tips for your application 🫡
Show Your Data Skills: Make sure to highlight your experience with Python, SQL, and data warehousing in your application. We want to see how you've used these skills in a business context, so don’t just list them—give us examples of how you’ve translated data into client-ready insights.
Tailor Your Application: Don’t send a generic application! Take the time to tailor your CV and cover letter to reflect the specific requirements of the Distribution Data Analyst role. Mention your understanding of RFP/DDQ processes and how your experience aligns with what we’re looking for.
Be Clear and Concise: When writing your application, clarity is key. Use straightforward language to explain your past experiences and achievements. We appreciate well-structured applications that get straight to the point—show us how you can communicate complex data simply!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets to the right people. Plus, it shows us you’re genuinely interested in joining our team at StudySmarter!
How to prepare for a job interview at Ramsey Portia
✨Know Your Data Inside Out
Make sure you’re familiar with the data tools mentioned in the job description, like Python and SQL. Be ready to discuss how you've used these tools to create client-ready outputs or reports in previous roles.
✨Understand the RFP/DDQ Landscape
Brush up on your knowledge of RFP and DDQ processes. Being able to speak confidently about how data plays a role in these processes will show that you understand the business context and can contribute effectively.
✨Showcase Your Commercial Mindset
Prepare examples of how you've translated complex data into insights that have driven business decisions. Highlight any experiences where your work directly impacted client acquisition or retention.
✨Engage with Stakeholders
Demonstrate your ability to communicate with non-technical stakeholders. Think of instances where you’ve made data accessible and meaningful for others, as this is crucial for the role.