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Experience
Experienced
About the role
This role sits within a bank's data and AI organisation, owning the pipelines and infrastructure that support machine learning and agentic products. You will work alongside data, engineering, AI and platform teams to build governed, scalable data foundations. It suits a senior data engineering specialist who enjoys large-scale architecture and cross-team collaboration.
What you'll do
Shape the bank's AI data architecture and set data engineering standards.
Define reusable patterns for ingesting, transforming, enriching, storing and retrieving structured and unstructured data.
Contribute to enterprise data standards, semantic modelling frameworks and architecture blueprints.
Work with architecture, platform and AI teams on the future data ecosystem.
Develop and maintain batch and streaming pipelines using distributed processing technologies.
Optimise data workflows on lakehouse platforms and distributed compute environments.
Build retrieval-ready and feature-ready datasets for AI and machine learning.
Develop semantic layers, ontologies, taxonomies and knowledge representations.
Design solutions using graph databases, vector stores, document stores and other non-relational technologies.
Implement data quality controls, lineage, observability, monitoring and operational support.
Handle ingestion, parsing, chunking, enrichment and normalisation of unstructured and multimodal content.
Apply security, governance, privacy and data protection controls across the data lifecycle.
Ensure solutions meet enterprise governance, regulatory and operational resilience standards.
Promote data quality, traceability, auditability and reliability practices.
Maintain documentation, architecture artefacts, engineering standards and operational runbooks.
Support architecture reviews and technology governance processes.
Uphold the Group's Values and Code of Conduct, and manage risk, conduct and compliance matters.
Good to know
Based in Singapore.
Degree in Computer Science, Engineering, Data Engineering, Information Systems or a related discipline, or equivalent practical experience.
Requirements
12+ years in data engineering, software engineering, data platforms or distributed systems
Track record delivering enterprise-scale data solutions
Experience building and operating large-scale batch and streaming pipelines
Strong expertise in Spark, Databricks or similar distributed compute platforms
Experience with lakehouse architectures, object storage and formats such as Parquet, Delta Lake or Iceberg
Understanding of data modelling and storage technologies including graph, vector, document, key-value and wide-column databases
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