Data Engineer - Investment Management / Financial Services

£90,000 to £120,000 per annum Plus Bonus and Benefits

Our client is an Investment Management company based in Central London. They are embarking on a multi-year transformation which includes the delivery of a new data platform and as such have created a new role for a Data Engineer. This is an opportunity to be part of the design and implementation of the data platform as their first dedicated Data Engineer, working alongside selected vendors/service providers. The role will be an important contributor to architecture decisions, working practices, technical design and implementation of new features.

Key Responsibilities:

• Actively participate in the Data Platform implementation project, defining and implementing a fit for-purpose architecture in accordance with business needs.
• Implement necessary data process steps (ingestion, storage/warehousing, curation) for a front-to back data lifecycle for key data domains.
• Develop pipelines able to support the range of data sources in scope (on-premise SQL/Oracle, SaaS/cloud hosted data sources or vendors via API, file, Snowflake share or proprietary request formats (e.g. Bloomberg DL).
• Collaborate on data TOM and help maintain as it evolves.
• Build an understanding of the existing data flows, enabling involvement in BAU issue resolution.
• Work with multiple departments to align tools and working practices. Advise on best practice and new ways of working to enable the business to benefit from the data platform.
• Participate in issue investigations (i.e. SQL ad hoc reports for trade enquiries) and increase understanding of platform data flows and customisations.

Skills and Experience Required:

• Experience of working with Snowflake and setting up Snowflake Infrastructure.
• Detailed knowledge and hands-on experience of implementing cloud-based distributed and scalable solutions, leveraging modern compute, storage, databases and reporting technologies.
• Experience and understanding of data engineering projects through all stages and states within a financial institution.
• Strong technical skillset, including expertise in data modelling, experimental design, and execution.
• Experience with BI tools such as Microsoft BI.
• Understanding of CI/CD pipelines and DevOps practices for data engineering.
• Experience in data integration, ETL/ELT pipeline, and automation/orchestration.
• Experience of financial applications and knowledge of investment management practices and asset class financial data preferred.