Data & AI - Data Engineer
This role involves building and operating the data foundation for client AI and analytics solutions, including designing ingestion pipelines, lakehouse architectures, and governed semantic layers. The engineer will work closely with AI engineers and data scientists to ensure data is audit-ready and optimized for AI consumption.
- Compensation
- Not disclosed
- Location
- Singapore
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5–10 years of strong, current, hands-on data-engineering experience building and operating production data pipelines and platforms
- Experience delivering data work end-to-end to a production standard
- Client-facing or executive-stakeholder delivery experience
Nice to have
- Expert Python and SQL skills
- Production experience with dbt
- Hands-on delivery experience with Snowflake and/or Databricks
- Understanding of open table and lakehouse storage formats such as Apache Iceberg and Delta Lake
- Experience with dimensional modelling and data-vault modelling
- Production experience with data-orchestration tools like Dagster, Apache Airflow or dbt Cloud
- Experience with batch and streaming ingestion
- Experience with change-data-capture patterns
- Experience designing semantic layers using tools like Cube, dbt Semantic Layer or LookML
- Familiarity with knowledge-architecture technologies like Neo4j, SPARQL or RDF
- Experience with data-quality frameworks like GX Core or dbt tests
- Experience with data cataloguing and lineage tools like Atlan, Collibra or OpenMetadata
- Hands-on delivery experience on Azure, AWS or GCP
- Experience with infrastructure-as-code like Terraform
- Understanding of vector stores and embeddings for AI systems
- Knowledge of Asia-Pacific data privacy and residency regulations
Full description
Data & AI - Data Engineer
CFGI is standing up its Data & AI build team in Singapore and is seeking a hands-on Data Engineer to build and operate the data foundation on which every client artificial-intelligence solution depends.
Roughly eighty per cent of the effort in any artificial-intelligence programme is the data, so this is a central, load-bearing role for an engineer who can design and run governed, audit-ready pipelines that make client data ready for analytics and for artificial intelligence.
This is a Manager-level role based in Singapore, working with the founding Data & AI build team — the artificial-intelligence engineers, data scientists and, as the team grows, the artificial-intelligence architect — as well as CFGI’s APAC and global Data & Analytics and controls practices and client finance, technology and data stakeholders.
The role is hybrid in Singapore, with occasional travel across Asia-Pacific for client work. Work-authorisation sponsorship may be supported where required.
Healthcare and life sciences is the priority market, with delivery extending across CFGI’s other priority sectors — technology and software, real estate and real-estate investment trusts, and industrials — with private equity engaged as a cross-cutting channel rather than a standalone sector.
What you might expect:
- Build and operate the data foundation that every client artificial-intelligence and analytics solution runs on.
- Design and engineer the ingestion, pipelines, lakehouse, semantic layer, and data-quality and governance controls that turn fragmented client data into authoritative, lineage-traceable, artificial-intelligence-ready data.
- Work hand-in-hand with the artificial-intelligence engineers, preparing and serving the data their agents, retrieval systems and models depend on, and build to a client-grade, audit-ready standard from the first engagement.
- Design, build and operate data ingestion and integration pipelines from client source systems — enterprise resource planning, customer systems, clinical and operational systems, files and application programming interfaces — into a governed analytics and artificial-intelligence platform, using batch and streaming as required.
- Build and maintain the lakehouse or warehouse and its transformation layer, modelling data for both business-intelligence consumers and artificial-intelligence consumers, with tested, version-controlled transformations and clear, documented data models.
- Design and maintain a governed semantic layer so that business logic, metrics and definitions are consistent and reusable across dashboards, analytics and artificial-intelligence systems rather than re-implemented and diverging in each.