Our client KPMG is seeking an experienced AI Data Engineer to design, build, and operate enterprise-scale data pipelines and AI-ready data products that power advanced analytics, machine learning, and Generative AI solutions across a large financial technology environment. The successful consultant will play a key role in enabling production-ready AI by ensuring high-quality, scalable, secure, and governed data platforms that support both real-time and batch processing.

Working closely with AI Platform Engineering, Data Platform teams, MLOps, AI Application Engineers, Security, Risk, and business stakeholders, the consultant will develop robust data engineering capabilities that accelerate AI innovation while maintaining enterprise governance, privacy, and operational excellence.

The ideal consultant will have extensive experience designing cloud-native data platforms, building AI and machine learning data pipelines, and delivering enterprise data engineering solutions within financial services, fintech, telecommunications, or other highly regulated environments.

Key Activities

• Design and implement scalable AI data architectures supporting machine learning, Generative AI, and advanced analytics workloads.
• Build and maintain enterprise batch and real-time data pipelines.
• Develop feature engineering pipelines and enterprise feature stores to support reusable AI capabilities.
• Create curated AI-ready datasets for data scientists, AI engineers, and analytics teams.
• Design scalable data ingestion pipelines for structured and unstructured data sources.
• Implement automated data quality controls covering accuracy, completeness, consistency, and timeliness.
• Establish end-to-end data lineage, traceability, observability, and monitoring across AI data platforms.
• Detect and resolve data drift, anomalies, and pipeline performance issues.
• Implement enterprise data privacy, masking, access controls, and regulatory compliance requirements.
• Collaborate with AI Platform Engineering, MLOps, and AI Application teams to accelerate model deployment and productionisation.
• Standardise AI data engineering patterns, frameworks, and reusable data products across the organisation.
• Contribute to enterprise AI and data platform roadmaps while driving continuous improvements in reliability, scalability, and governance.

Your Background

Essential

• 5+ years of experience in Data Engineering or enterprise data platform engineering.
• Proven experience designing and building large-scale batch and streaming data pipelines.
• Hands-on experience developing feature engineering pipelines and AI-ready datasets.
• Strong experience with Microsoft Azure cloud data platforms.
• Experience working with structured and unstructured datasets.
• Strong understanding of data modelling, orchestration, and distributed data processing.
• Experience implementing data quality, lineage, observability, and governance frameworks.
• Strong understanding of security, privacy-by-design, and enterprise data governance.
• Experience supporting AI, machine learning, or advanced analytics platforms.
• Excellent problem-solving, collaboration, and stakeholder engagement skills.
• Ability to produce high-quality technical documentation and solution designs.

Desirable

• Experience within financial services, fintech, telecommunications, or other regulated industries.
• Exposure to Generative AI and enterprise AI platforms.
• Experience supporting MLOps environments and AI production workloads.
• Experience contributing to enterprise AI platform or cloud transformation initiatives.
• Master's degree in Computer Science, Data Engineering, Information Systems, Engineering, Artificial Intelligence, or a related discipline.
• Experience building cloud-native data products and reusable AI platform capabilities.
• Familiarity with enterprise architecture and AI governance frameworks.