Data & AI - Data Engineer

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.

Prepare and serve artificial-intelligence-ready data for the engineering team — including curated datasets, embeddings source data, feature inputs and retrieval corpora that retrieval-augmented-generation and model builds depend on.

Partner with the artificial-intelligence engineers on data contracts and interfaces.

Own data quality, master data and lineage — quality rules and validation, entity resolution and golden records where needed, and end-to-end lineage from source through to report and control so that outputs are trustworthy and audit-ready.

Embed data governance and privacy from the outset, including access controls, data classification, de-identification where required, and compliance with Singapore’s Personal Data Protection Act and the relevant Asia-Pacific cross-border-transfer and data-residency rules for each engagement, aligned to the firm’s control framework.

Carry real ownership of data architecture at engagement scale and help set the data-engineering standard for the practice.

Contribute to the practice’s reusable components, including pipeline templates, accelerators and standards.

Help create the reusable pipelines and accelerators that allow the team to scale from bespoke builds to a repeatable delivery model.

Mentor junior data resources as the team grows.

What you must have

Approximately 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, including designing, building and operating governed data pipelines and platforms.

Client-facing or executive-stakeholder delivery experience, including the ability to explain data design and technical trade-offs clearly to both technical colleagues and non-technical stakeholders.

What sets you apart

Expert Python and SQL skills.

Production experience with a transformation and modelling framework, particularly dbt, and disciplined, tested, version-controlled data transformation.

Hands-on delivery experience with Snowflake and/or Databricks.

Working understanding of open table and lakehouse storage formats such as Apache Iceberg and Delta Lake.

Experience with dimensional modelling and data-vault modelling for analytics.

Production experience with a data-orchestration tool such as Dagster, Apache Airflow or dbt Cloud.

Experience with batch and streaming ingestion.

Understanding of the distinction between event transport, such as Apache Kafka, and stream processing, such as Apache Flink.

Experience with change-data-capture patterns.

Experience designing semantic layers using tools or approaches such as Cube, the dbt Semantic Layer or LookML to establish consistent, governed business logic across business-intelligence and artificial-intelligence consumers.

Awareness of ontologies and knowledge graphs for structured knowledge representation in artificial-intelligence-enabled systems.

Familiarity with the Resource Description Framework data model, SPARQL, property-graph databases such as Neo4j, or comparable knowledge-architecture technologies.

Experience with data-quality and validation frameworks such as GX Core, formerly Great Expectations, dbt tests, or