Data Engineer, Snowflake, Investment Management, Asset Management, Financial Services, Banking, Data Engineering, Datamodelling, MS Power BI, MS Power Automate, Python, SQL, Cloud, Azure.

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 life cycle 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 (eg 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 (ie 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 skill set, including expertise in Datamodelling, 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.