At a Glance
- Tasks: Build and enhance scalable data solutions using Snowflake, AWS, Python, and SQL.
- Company: Join a global investment bank with a growing Data Engineering function.
- Benefits: Competitive salary up to £120k plus bonus, hybrid working model.
- Other info: Collaborate with Market Data, Quant, and Risk teams for excellent career growth.
- Why this job: Make an impact on critical data infrastructure in the financial sector.
- Qualifications: Experience in Data Engineering with strong skills in Snowflake, AWS, Python, and SQL.
The predicted salary is between 63000 - 77000 £ per year.
Location: London
Working: Hybrid (3 days/week in office)
Salary: Up to £120k + bonus
The Role
Joining a global investment bank as part of a growing Data Engineering function, working on critical data infrastructure across the business. You’ll build and enhance modern data solutions using Snowflake, AWS, Python and SQL, working with large-scale financial and historical data. The role sits closely with Market Data, Quant and Risk teams, with your work supporting areas including market risk analytics, VaR and stress testing.
Key Responsibilities
- Build and enhance scalable data pipelines and infrastructure
- Develop Python ETL/ELT pipelines and complex SQL models
- Design and optimise Snowflake data solutions
- Integrate and manage large-scale financial and historical data
- Identify and resolve data quality and data integrity issues
- Work closely with Data, Quant, Market Data and Risk teams
- Ensure data solutions are scalable, reliable and auditable
Key Requirements
- Proven experience in a Data Engineering / Data Development role
- Strong hands-on experience with Snowflake
- Strong AWS, Python and SQL skills
- Experience working with large-scale financial, market or time-series data
- Exposure to market data, market risk or risk analytics highly beneficial
- Understanding of concepts such as VaR, SVaR, sensitivities or stress testing advantageous
- Strong analytical and problem-solving skills
- Excellent communication and stakeholder management skills
No sponsorship available. Get in touch for more details.
AVP Data Engineer in London employer: McGregor Boyall Careers
As an IT Project Manager in our dynamic technology team, you will thrive in a collaborative work culture that values innovation and excellence. We offer competitive benefits, ongoing professional development opportunities, and the chance to lead impactful network infrastructure projects in a complex enterprise environment. Join us to make a meaningful contribution while advancing your career in a supportive and growth-oriented setting.
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We think this is how you could land AVP Data Engineer in London
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We think you need these skills to ace AVP Data Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at McGregor Boyall Careers. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at McGregor Boyall Careers
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
