VP Data Engineering Lead — Snowflake/AWS/Python, Hybrid

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

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.