Senior Data Engineering (GCP)

We are looking for an experienced Senior Data Engineer to support the delivery of an AI-powered conversational analytics capability for a large enterprise client. This role will be critical in preparing complex business data for AI consumption, developing semantic layers, and ensuring data is structured for accurate, efficient, and governed querying by LLM-driven workflows. The ideal candidate will have strong hands-on experience in modern cloud data engineering, data modelling, semantic design, and building scalable pipelines for analytical and AI use cases. This person will work closely with AI Engineers, Software Engineers, Data Scientists, and DevOps teams to ensure the data foundation supports natural language querying, narrative insight generation, anomaly detection, and future scalability.

Responsibilities

• Design, build, and maintain scalable data pipelines to prepare complex business data for conversational analytics use cases.
• Develop and maintain semantic layers, business logic mappings, and data structures that improve AI understanding of client taxonomies, KPIs, hierarchies, and business concepts.
• Model and transform complex business data into structures optimised for query generation, interpretation, and insight production.
• Partner with AI and software engineering teams to support LLM workflows, agent orchestration, and governed access patterns.
• Implement robust ingestion, transformation, and data quality processes for structured analytical datasets.
• Support live-query and cached data access patterns depending on agreed architecture and performance needs.
• Ensure data is accessible, explainable, and aligned to business definitions used in evaluation and user acceptance testing.
• Collaborate with Data Scientists to support ground-truth evaluation, validation datasets, and regression testing.
• Work with architects and client data stakeholders to align designs with enterprise data standards, governance requirements, and long-term maintainability.
• Contribute to production readiness through documentation, testing, monitoring, and knowledge transfer to internal teams.
• Support deployment of data solutions into controlled Dev, Test, and Production environments.
• Help shape scalable patterns for future expansion into additional datasets and more advanced analytical capabilities.