Data Engineer

About Rimes

Rimes provides the Intelligence Fabric for Capital Markets, a trusted data network and intelligence architecture that transforms fragmented data, operations and workflows into decision-grade intelligence. The world’s leading institutional investors, asset managers, and service providers rely on Rimes to help them make better investment decisions that power more than US$ 75 trillion in AUM annually.

The Opportunity:

Rimes is looking for a Data Engineer to actively participate in building and modernising the data platform that underpins our entire data ecosystem. You will work alongside the Data Engineering Team Lead, who sets overall direction and owns the platform roadmap, contributing hands-on engineering across platform development, tooling, data modelling, and operational improvement. 

The core of this role is building reusable, scalable capabilities that allow the team to craft high-quality financial data pipelines efficiently, rather than building pipelines one by one. We are also looking for engineers curious about agentic AI workflows and how they can automate and enhance the way data platforms operate. 

Core Responsibilities:

• Platform Development and Modernisation: Actively participate in the transformation of our existing data platform and pipelines, leveraging modern technologies such as Snowflake and Databricks to improve scalability, performance, and efficiency. 
• Tooling and Automation: Build and extend tooling to support the seamless ingestion and quality assurance of financial data. Automate repetitive or error-prone processes to reduce manual intervention and improve operational efficiency across the data engineering workflow. 
• Data Model Design: Contribute to the design and implementation of scalable, reusable data models for financial data, ensuring the data architecture supports a wide range of business use cases. Work within the standards and patterns set by the team to maximise consistency and the long-term value of the company’s data products. 
• Hands-On Engineering: Play an active role in day-to-day engineering tasks, coding, reviewing, and designing complex data solutions. Share knowledge and best practices with peers through code review and technical discussion, contributing to a culture of engineering excellence without a formal management remit. 
• Operational Efficiency: Take part in efforts to minimise the operational costs of data ingestion and pipeline support. Identify and implement optimisations in both technical workflows and the processes used by support personnel, reducing toil and improving reliability. 
• Collaboration: Work closely with cross-functional teams including Product, Data Onboarding, Data Quality, and Operations to ensure data engineering solutions meet both technical and business needs. Communicate clearly about trade-offs, timelines, and dependencies.