Data Engineer - Remote

• 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.

• 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.

• Expert Python and SQL skills.

Nice to have:

• Healthcare and life-sciences data experience involving clinical, claims or real-world-evidence data.

• Experience with the interoperability standards and clinical ontologies that structure healthcare data.

• Familiarity with Fast Healthcare Interoperability Resources (FHIR), itself a Health Level Seven standard, and legacy Health Level Seven version 2 messaging. • Familiarity with healthcare terminologies and ontologies such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), the International Classification of Diseases (ICD-10 and ICD-11), Logical Observation Identifiers Names and Codes (LOINC), RxNorm for medicines, and the Medical Dictionary for Regulatory Activities (MedDRA) for pharmacovigilance.

• Experience with the Observational Medical Outcomes Partnership (OMOP) Common Data Model for harmonising real-world evidence across sources.

• Familiarity with Digital Imaging and Communications in Medicine (DICOM) for medical imaging.

• Office-of-the-Chief-Financial-Officer data experience involving financial close and reporting, consolidation, controls or risk data — CFGI’s core buyer context.

• Experience building data foundations in a governed, audit-ready environment.

• Experience contributing reusable data accelerators to a growing practice.

• Receive 100% salary from the onboarding date.

• Participate in company activities: Teambuilding, travel, vacation and other activities.

• Work with large and advanced systems, have the opportunity to develop comprehensive technology skills with complex problems, requiring high accuracy.