Data Engineer
Summary
Build and operate enterprise-scale ETL/ELT data pipelines and knowledge graph infrastructure using Python, SQL, Google Spanner, BigQuery, Neo4j, and vector databases to support enterprise search and AI systems.
We are looking for experienced Data Engineers to build and operate enterprise-scale data pipelines that transform disparate organisational knowledge into structured, searchable and AI-ready formats. You will work across ingestion, enrichment, indexing and storage, helping create the underlying data infrastructure required to support enterprise search, knowledge graphs and AI systems.
Key Responsibilities
- Build and operate large-scale data ingestion and transformation pipelines.
- Develop ETL/ELT processes for disparate enterprise data sources.
- Design data structures supporting enterprise knowledge and AI use cases.
- Develop and maintain knowledge graph capabilities.
- Work with graph and vector-based data technologies.
- Implement metadata management and data cataloguing capabilities.
- Support enterprise search indexing and information retrieval.
- Ensure data quality, governance and lineage across the platform.
- Design event-driven data processing solutions.
- Optimise data pipelines and platforms for performance and scalability.
- Work with distributed data systems and cloud data platforms.
- Collaborate with Data Scientists, Backend Engineers and business stakeholders to make enterprise knowledge accessible and usable.
Key Skills & Experience
- Python and SQL
- Google Spanner
- Neo4j / Graph Databases
- Knowledge Graph Engineering
- ETL / ELT pipelines
- BigQuery
- Data modelling
- Metadata management
- Data cataloguing
- Vector databases
- Event-driven architecture
- Enterprise search indexing
- Data governance and quality
- Data lineage
- Distributed systems
- Performance and scalability optimisation