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# VP Data Engineer

- **Company:** The Borough
- **Location:** London, UK
- **Salary:** £150,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Information Engineering, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Systems, Database Development, Python (Programming Language), SQL Databases, Snowflake, Data Pipelines
- **Published:** September 21, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/vp-data-engineer/47792413

## About the Role

teamsEnsure data solutions are scalable, reliable and auditableKey RequirementsProven experience in a Data Engineering / Data Development roleStrong hands-on experience with SnowflakeStrong AWS, Python and SQL skillsExperience working with large-scale financial, market or time-series dataExposure to market data, market risk or risk analytics highly beneficialUnderstanding of concepts such as VaR, SVaR, sensitivities or stress testing advantageousStrong analytical and problem-solving skillsExcellent communication and stakeholder management skillsNo sponsorship available.Get in touch for more details - #####

## Description

150000.00 - 150000.00 per annumLondon, London, WC2N 5DU, England, United KingdomPermanentVP Data Engineer Financial Technology Location: London Working: Hybrid (3 days/week in office) Salary: Up to 150k + bonusThe RoleJoining 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 ResponsibilitiesBuild and enhance scalable data pipelines and infrastructureDevelop Python ETL/ELT pipelines and complex SQL modelsDesign and optimise Snowflake data solutionsIntegrate and manage large-scale financial and historical dataIdentify and resolve data quality and data integrity issuesWork closely with Data, Quant, Market Data and Risk


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