VP Data Engineer

Mcgregor Boyall; Greater London; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

Mcgregor Boyall is seeking a VP Data Engineer in London to join a global investment bank’s data engineering function. You will build data infrastructure across the business, using Snowflake, AWS, Python and SQL, handling large-scale financial and historical data.

You will work closely with Market Data, Quant and Risk teams to support analytics, VaR and stress testing while ensuring scalable, auditable data solutions in a hybrid work setup.

Qualifications

• 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 VaR, SVaR, sensitivities or stress testing advantageous.
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management skills.

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

Skills

Data engineering

SQL proficiency

Python programming

Big data handling

Stakeholder management

Tools

Snowflake

AWS

Job description

VP Data Engineer

Financial Technology

Location: London

Working: Hybrid (3 days/week in office)

Salary: Up to £150k + 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.