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- Tasks: Build scalable data solutions and maintain core data infrastructure for finance.
- Company: Leading financial services firm in Greater London with a focus on innovation.
- Benefits: Competitive salary, hybrid working model, and great benefits.
- Why this job: Join a dynamic team and make an impact in the finance sector with your data skills.
- Qualifications: Extensive SQL expertise, relevant degree, and strong analytical skills.
- Other info: Opportunity for career growth in a supportive environment.
The predicted salary is between 36000 - 60000 £ per year.
A leading financial services firm in Greater London is seeking a Data Engineer to build scalable data solutions and maintain core data infrastructure. The role involves engineering robust data pipelines, ensuring reliable data for Finance, and working with Microsoft Fabric and Power BI.
Candidates should have:
- Extensive SQL expertise
- A Bachelor's degree in a relevant field
- Strong analytical skills
This full-time fixed term contract offers a competitive salary and benefits, with a hybrid working model.
Finance Data Engineer (Contract) — Modern Data Pipelines employer: Moneycorp Bank Limited
Contact Detail:
Moneycorp Bank Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Finance Data Engineer (Contract) — Modern Data Pipelines
✨Tip Number 1
Network like a pro! Reach out to folks in the finance and data engineering space on LinkedIn. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and projects. This is your chance to shine and demonstrate your SQL expertise and analytical skills.
✨Tip Number 3
Prepare for the interview by brushing up on Microsoft Fabric and Power BI. We want you to be ready to discuss how you can build scalable data solutions that meet their needs.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take that extra step.
We think you need these skills to ace Finance Data Engineer (Contract) — Modern Data Pipelines
Some tips for your application 🫡
Show Off Your SQL Skills: Make sure to highlight your extensive SQL expertise in your application. We want to see how you've used SQL in past projects, so don’t hold back on the details!
Tailor Your CV: Customise your CV to reflect the skills and experiences that align with the Finance Data Engineer role. We love seeing candidates who take the time to match their background with what we’re looking for.
Be Clear and Concise: When writing your cover letter, keep it clear and to the point. We appreciate straightforward communication, so make sure you convey your passion for data engineering without any fluff.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Moneycorp Bank Limited
✨Know Your SQL Inside Out
Make sure you brush up on your SQL skills before the interview. Be prepared to discuss complex queries and how you've used SQL in past projects. Practising common SQL problems can really help you stand out.
✨Familiarise Yourself with Microsoft Fabric and Power BI
Since the role involves working with Microsoft Fabric and Power BI, it’s crucial to understand their functionalities. Try to showcase any previous experience or projects where you’ve used these tools, as this will demonstrate your hands-on knowledge.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities. Think of scenarios where you had to build data pipelines or troubleshoot data issues. Use the STAR method (Situation, Task, Action, Result) to structure your answers effectively.
✨Showcase Your Analytical Skills
Highlight your analytical skills by discussing specific examples where your analysis led to significant improvements or insights. This will show the interviewers that you can not only handle data but also derive meaningful conclusions from it.
