Data Engineer

London | Boutique Investment Fund | Data, AI & Modern Engineering What if you could build the data foundations of an investment firm before the legacy systems, technical debt and years of compromise arrive? We are partnering with a successful London-based alternative investment manager specialising in credit, managing a multi-billion-euro portfolio for institutional investors globally. The firm is growing, investing heavily in technology and preparing its platform for an increasingly data and AI-driven future. Rather than attempting to bolt AI onto decades of legacy technology, they are taking a different approach: building a modern data architecture from a clean sheet, designed from the outset to support analytics, automation, AI and the next generation of investment technology. They are now looking for a talented Data Engineer to play a key role in building it. Opportunity This is a hands-on engineering position with genuine ownership.You will help design, build and scale the firm's new data platform, creating the pipelines and datasets that bring together information from internal systems, external sources, documents and emails into a modern Lakehouse architecture.But this isn't simply about moving data from A to B. The platform is being built to support the firm's future AI and machine-learning capabilities, including retrieval and embedding pipelines, LLM-powered applications, automated extraction and classification, and multi-agent workflows.You will work closely with both technology and investment/business stakeholders, allowing you to understand the business and the problems you're solving rather than operating several layers removed from the end user. What You'll Be Building Building robust ELT/data pipelines from internal applications and third-party sources into the Lakehouse.Transforming raw information into clean, reliable and well-documented datasets for analytics, reporting and new products.Preparing unstructured data, including documents and emails, for AI applications.Developing retrieval, embedding and enrichment pipelines supporting LLM-based applications.Incorporating LLMs into data workflows for extraction, classification and enrichment.Helping develop multi-agent workflows, where models and agents coordinate to solve more complex tasks.Owning monitoring, alerting, data-quality checks and pipeline reliability.Working directly with stakeholders to turn real business requirements into well-engineered technical solutions. Technology The environment is centred around a modern Microsoft/Azure data stack, including: Python | SQL | Azure | Databricks | Azure Data Lake Storage | Microsoft Foundry | Microsoft Fabric | Delta Lake | GitHub | Terraform The team is also actively embracing modern AI-assisted software engineering, using tools such as Claude Code and Cursor as part of the development workflow. They aren't looking for someone who simply uses AI to generate code. They want an engineer who understands how these tools can make them faster and more effective while maintaining high standards of engineering quality. What We're Looking For Around 3–7 years of professional engineering experience, most likely within Data Engineering, Analytics Engineering or Software Engineering with a strong data focus.You'll need strong Python and SQL, good software-engineering fundamentals and experience building reliable production data pipelines.You'll understand concepts such as schema design, incremental loading, idempotency and schema evolution, and you'll be comfortable working within structured engineering practices using Git, branching, pull requests and code reviews.Experience with LLMs, AI-assisted development or AI-enabled data pipelines is particularly interesting.Knowledge of Databricks, Delta Lake, Lakehouse/Medallion architectures, Airflow, CI/CD or Terraform would all be valuablePrevious financial services experience is desirable but not essentialPassionate about engineering and building products Why This Is Different There are plenty of Data Engineering roles maintaining platforms somebody else designed years ago. This isn't one of them. You will be joining while the foundations are still being built, allowing you to influence how the platform evolves and how data and AI are ultimately used across the investment business. You'll have the resources of a successful multi-billion-euro investment manager, but the visibility, ownership and pace that come from working within a close-knit boutique environment. For an ambitious Data Engineer who wants to move beyond maintaining yesterday's technology and help build the infrastructure for an AI-enabled investment firm, this is a rare opportunity.