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Connor, Clark & Lunn Financial Group

Data Analyst

Data EngineeringConfirmed live in the last 24 hours
Full-timeLondon, UK£55k - £70k

About the Job

Requirements

  • Strong programming skills in Python and SQL, with experience in application development, automation, or data-focused projects.
  • Passion for AI technologies, including large language models, AI agents, and AI-powered application development.
  • Experience with Microsoft data and analytics platforms such as Microsoft Fabric, Azure, and Power BI.
  • Knowledge of SharePoint, Microsoft Graph, Microsoft 365 integrations, and enterprise data ecosystems.
  • Understanding of data engineering concepts, including ETL pipelines, database design, APIs, cloud services, and application development.
  • Familiarity with modern AI development tools such as LLM APIs, RAG architectures, vector databases, and agent frameworks.
  • Experience with software development best practices, including Git, testing, and AI coding assistants.

Responsibilities

  • Develop and execute investment data strategies to improve the accessibility, quality, and usability of research and portfolio information.
  • Build and maintain data pipelines, databases, and data models that integrate information from multiple platforms, including Microsoft Fabric, SharePoint, and other internal systems.
  • Use Python, SQL, and modern data engineering tools to automate data processing, analytics, and workflow optimization.
  • Collaborate with data, technology, and AI engineering teams to integrate portfolio, trading, performance, and research data sources.
  • Design AI-powered solutions that enable intelligent access to investment research, meeting notes, and decision-making history.
  • Experiment with and implement emerging AI technologies, including large language models, retrieval-augmented generation (RAG), semantic search, document processing, and AI agents.
  • Develop practical applications, dashboards, and user-friendly tools that help investment professionals analyze data, monitor companies, and prepare for meetings.
  • Rapidly prototype, test, and refine solutions based on user feedback, then transition successful concepts into scalable, production-ready applications.
  • Partner with portfolio managers and analysts to identify opportunities for data and AI-driven improvements and translate business needs into technical solutions.

About the Company

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