McGregor Boyall
AVP Data Engineer

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AVP Data Engineer
Financial Technology
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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


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Get in touch for more details - ncarolan@mcgregor-boyall.com
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