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For Companies At a Glance
- Tasks: Analyse complex financial data and improve reporting processes.
- Company: Established financial services organisation with a supportive culture.
- Benefits: Friendly work environment, varied workload, and career development opportunities.
- Other info: Opportunity to work on cross-functional projects and enhance data accuracy.
- Why this job: Make an impact in financial management while working with senior stakeholders.
- Qualifications: Degree in a numerate discipline and strong analytical skills.
The predicted salary is between 35000 - 45000 £ per year.
An established and well-regarded financial services organisation is looking to appoint a Financial Data Analyst to join its Actuarial / Financial Reporting function. This role would suit a numerate, detail-focused analyst who enjoys working with complex financial data, improving controls and processes, and supporting reporting within a regulated environment. The position offers broad exposure across valuations, management information, and project work, with regular interaction with senior stakeholders.
The Role
- You will be responsible for supporting the financial and risk management activities of the business, with a particular focus on data quality, reconciliation, and reporting.
- Key responsibilities will include:
- Owning the data reconciliation process for monthly and year-end actuarial valuations
- Identifying and implementing process improvements to enhance data accuracy and efficiency
- Developing and maintaining a robust control framework around financial data used for actuarial purposes
- Producing and updating financial and risk dashboards for senior management, Boards, and Committees
- Supporting regular management information (MI) reporting
- Leading demographic experience investigations and contributing to annual experience analysis
- Supporting cross-functional projects, responding to data and analysis queries as required
- Maintaining clear documentation of processes, controls, and procedures
The Candidate
The successful candidate is likely to have a strong analytical mindset and experience working with financial datasets in a structured, regulated environment.
Essential requirements:
- Degree-level qualification (or equivalent) in a numerate discipline
- Experience analysing, summarising, and reconciling financial data
- Strong Excel capability and high level of computer literacy (SQL desirable)
- Excellent numeracy, attention to detail, and problem-solving skills
In return you will be rewarded with a friendly, supportive working environment with a varied and interesting workload within a highly respected and established organisation.
Financial Data Analyst in Manchester employer: Front Row Recruitment
Contact Detail:
Front Row Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Financial Data Analyst in Manchester
✨Tip Number 1
Network like a pro! Reach out to people in the financial services sector, especially those who work as Financial Data Analysts. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Prepare for interviews by brushing up on your Excel skills and understanding financial data analysis. We recommend practising common interview questions related to data reconciliation and process improvements to show you’re the right fit.
✨Tip Number 3
Don’t just apply anywhere; focus on companies that align with your values and career goals. Use our website to find roles that excite you and match your skills, making your application stand out!
✨Tip Number 4
Follow up after interviews! A quick thank-you email can leave a lasting impression. It shows your enthusiasm for the role and keeps you fresh in their minds as they make their decision.
We think you need these skills to ace Financial Data Analyst in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Financial Data Analyst role. Highlight your analytical mindset and any experience with financial datasets, as this will show us you’re a great fit for the position.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you’re passionate about working with complex financial data. Share specific examples of how you've improved processes or enhanced data accuracy in previous roles to grab our attention.
Showcase Your Technical Skills: Don’t forget to mention your strong Excel capabilities and any experience with SQL. We love seeing candidates who are tech-savvy and can handle data like a pro, so make sure these skills shine through in your application.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process!
How to prepare for a job interview at Front Row Recruitment
✨Know Your Numbers
Brush up on your financial data analysis skills before the interview. Be prepared to discuss specific examples of how you've reconciled financial data or improved processes in previous roles. This will show that you can handle the responsibilities of the Financial Data Analyst position.
✨Excel is Your Best Friend
Since strong Excel capability is essential for this role, make sure you're comfortable with advanced functions and data manipulation techniques. You might even want to practice a few Excel scenarios beforehand, so you can confidently demonstrate your skills if asked.
✨Understand the Business
Research the financial services organisation thoroughly. Understand their products, services, and the regulatory environment they operate in. This knowledge will help you tailor your answers and show that you're genuinely interested in contributing to their success.
✨Prepare for Stakeholder Interaction
Given the regular interaction with senior stakeholders, think about how you can effectively communicate complex data insights. Prepare examples of how you've successfully presented data to non-technical audiences in the past, as this will highlight your ability to bridge the gap between data and decision-making.