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- Tasks: Lead data transformation projects and build robust data pipelines for financial insights.
- Company: Dynamic banking institution focused on innovation and data excellence.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Why this job: Shape the future of financial data and make impactful decisions in a collaborative environment.
- Qualifications: Strong data analysis skills, advanced SQL, and experience with financial datasets.
- Other info: Join a team that values proactive thinkers and offers excellent career advancement.
The predicted salary is between 48000 - 72000 £ per year.
An opportunity for a data focused leader to shape and deliver a large scale financial data transformation programme supporting strategic decision making across a banking business. The role centres on building robust data pipelines, owning the data warehouse architecture, and ensuring high quality data to power reporting, analytics and insights. Must have a strong understanding of Front Office Trading and Front Office Data.
Responsibilities:
- Lead the design, build and optimisation of the enterprise data warehouse, including sourcing, validation, ingestion and data architecture.
- Develop and manage automated ETL pipelines and workflows for financial datasets.
- Integrate data warehouse outputs with Salesforce, PowerBI, pricing platforms and other analytical endpoints.
- Ensure accuracy, consistency and scalability of historic and current datasets and align them to evolving data models.
- Create detailed business requirements documentation that supports technology delivery teams.
- Support the build of new data models that power internal management information and AI aligned analytics.
- Collaborate with internal teams and external providers to improve data quality and streamline data delivery processes.
- Drive enhancements in data governance and act as subject matter expert for all data related topics.
- Maintain data quality and client hierarchy within Salesforce in partnership with client onboarding teams.
Requirements:
- Strong experience in data analysis and enterprise level data systems.
- Advanced SQL expertise with the ability to build complex queries and tables.
- Hands on experience managing ETL processes and working to tight project timelines.
- Must have strong knowledge of financial data, accounting principles and CIB business information frameworks.
- Experience working with large and complex datasets in a financial context.
- Proficient in Python and experienced with at least one major cloud data warehouse platform such as Snowflake.
- Experience working within a large financial institution and educated to degree level in a STEM field.
- Experience using Salesforce or similar CRM platforms.
- Excellent communication skills, proactive mindset and the ability to work independently and collaboratively.
Apply now for immediate consideration. No sponsorship available; applicants must have the right to work in the region.
Front Office Data Analyst in London employer: Nicoll Curtin
Contact Detail:
Nicoll Curtin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Front Office Data Analyst in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those who work in data analysis or finance. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially any that relate to financial data or ETL processes. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and Python skills. Be ready to discuss how you've tackled complex datasets and built data pipelines in past roles. Confidence in your technical abilities can really impress hiring managers.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are eager to join us. Plus, it shows you're genuinely interested in being part of our team.
We think you need these skills to ace Front Office Data Analyst in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the Front Office Data Analyst role. Highlight your experience with data analysis, SQL, and any relevant financial data 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 explain why you're passionate about data and how you can contribute to our financial data transformation programme. Keep it concise but impactful – we love a good story!
Showcase Your Technical Skills: Don’t forget to mention your hands-on experience with ETL processes, Python, and cloud data warehouses like Snowflake. We’re keen on seeing how you’ve tackled complex datasets in your previous roles, so give us the details!
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. Plus, we love seeing applications come in through our own channels!
How to prepare for a job interview at Nicoll Curtin
✨Know Your Data Inside Out
Make sure you brush up on your knowledge of financial data and Front Office Trading. Be prepared to discuss how you've previously built and optimised data pipelines, as well as your experience with ETL processes. This will show that you understand the core responsibilities of the role.
✨Showcase Your Technical Skills
Be ready to demonstrate your advanced SQL expertise and any hands-on experience with Python. You might be asked to solve a problem or write a query on the spot, so practice beforehand. Familiarise yourself with cloud data warehouse platforms like Snowflake, as this could come up in conversation.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills and ability to work under tight project timelines. Think of specific examples from your past experiences where you successfully managed complex datasets or improved data quality. Use the STAR method (Situation, Task, Action, Result) to structure your answers.
✨Communicate Effectively
Since excellent communication skills are a must, practice explaining complex data concepts in simple terms. Be ready to discuss how you've collaborated with internal teams and external providers to enhance data governance. This will highlight your proactive mindset and ability to work both independently and as part of a team.