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For Companies At a Glance
- Tasks: Translate 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: Opportunity to engage directly with stakeholders and contribute to revenue generation.
- 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 Slough 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 Slough
✨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 comfortable with common interview questions related to data analysis and client-facing scenarios. Mock interviews with friends or mentors can really boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be just what you’re looking for. Plus, it’s a great way to show your interest in joining our team directly.
We think you need these skills to ace Distribution Data Analyst – Fund Management in Slough
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Distribution Data Analyst. Highlight your experience with Python, SQL, and any relevant data warehousing tools. We want to see how your skills can translate into client-ready insights!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're the perfect fit for this role. Share specific examples of how you've used data in a business context and how that led to successful outcomes. We love a good story!
Showcase Your Impact: When detailing your past experiences, focus on the impact you made rather than just listing tasks. We’re looking for evidence of ownership and how your work has contributed to client acquisition or retention. Make it clear how you’ve added value!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Let’s get your journey started!
How to prepare for a job interview at Ramsey Portia
✨Know Your Data Inside Out
Make sure you’re well-versed in 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 solve real-world problems.
✨Understand the RFP/DDQ Landscape
Familiarise yourself with the RFP and DDQ processes. Think of examples where you’ve contributed to these processes or how your data insights have supported client acquisition and retention.
✨Showcase Your Communication Skills
This role is all about translating complex data into clear insights. Prepare to demonstrate how you’ve effectively communicated technical details to non-technical stakeholders in previous roles.
✨Prepare Questions for Them
Think of insightful questions that show your interest in the company and the role. Ask about their current data challenges or how they envision the future of data in their client interactions.