Investment Data Engineer: AI & Data Platform

Cclfg

Greater London

On-site

GBP 55,000 - 70,000

Full time

3 days ago

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Benefits offered by this job

Flexible work practices

Continuous learning budgets

Comprehensive health benefits

Job summary

Connor, Clark & Lunn Financial Group is seeking a Data Analyst to help build an AI-enabled research and decision-support platform for investment teams in the UK. You will work with portfolio managers, analysts, and engineers to connect research, investment, and portfolio data while moving quickly in a collaborative environment.

You will develop data strategies, build scalable pipelines, and apply Python/SQL to automate processing.

Qualifications

Demonstrated builder mindset with a focus on rapidly developing, testing, and iterating practical solutions.
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 (LLMs), AI agents, and AI-powered application development.
Excellent problem-solving abilities and a willingness to learn new technologies and tackle unfamiliar challenges.
Self-motivated and able to take ownership of projects while working independently with minimal supervision.
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, AI coding assistants, collaboration, and continuous learning.

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.