Data & AI - Data Engineer

• 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 comparable tools.
• Experience with master-data and entity-resolution techniques.
• Experience with data cataloguing, lineage and data-contract tooling such as Atlan, Collibra, OpenMetadata, or comparable platforms for production-grade, audit-ready data.
• Hands-on delivery experience on at least one major cloud platform — Microsoft Azure, Amazon Web Services or Google Cloud Platform — and its associated data services.
• An appreciation of infrastructure-as-code, such as Terraform, and cost-aware platform design.
• A working understanding of how data feeds artificial-intelligence systems, including embeddings and vector stores such as pgvector, Pinecone, Weaviate or Qdrant.
• Experience or working knowledge of chunking and retrieval-corpus preparation for retrieval-augmented generation.
• Understanding of the difference between analytics-grade and artificial-intelligence-grade data preparation.
• Experience building authoritative, lineage-traceable data foundations with clear controls, reproducibility and retained evidence.
• A practical grasp of data governance, privacy and residency across the Asia-Pacific markets the practice serves, not Singapore alone.
• Working awareness of Singapore’s Personal Data Protection Act and how its cross-border-transfer requirements affect data architecture and design.
• Working awareness of the region’s principal data-protection and data-residency regimes and how they differ on cross-border transfer, including Japan’s Act on the Protection of Personal Information, South Korea’s Personal Information Protection Act, India’s Digital Personal Data Protection Act 2023, China’s Personal Information Protection Law and Data Security Law, Hong Kong’s Personal Data (Privacy) Ordinance, Australia’s Privacy Act and Australian Privacy Principles, and the developing Southeast Asian regimes in Thailand, Malaysia, Indonesia and the Philippines.
• The ability to design pipelines, classification controls and residency controls that hold up across different Asia-Pacific jurisdictions.
• 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.

Who thrives here:

• A hands-on data engineer who enjoys owning the data foundation from ingestion through governed consumption.
• Someone comfortable taking real ownership of data architecture at engagement scale rather than working only within a narrowly defined component.
• Strong engineering judgement with the ability to distinguish between approaches that work in development and those that are appropriate for production, governed client environments.
• Comfortable working across ingestion, pipelines, transformation, lakehouse and warehouse design, semantic layers, data quality, governance, lineage and artificial-intelligence-ready data.
• Able to work closely with artificial-intelligence engineers, data scientists and client stakeholders to make data usable across analytics and artificial-intelligence use cases.
• Strong analytical and problem-solving skills with the ability to explain design choices and trade-offs clearly.
• Entrepreneurial, self-motivated, adaptable, ethical and dependable.
• Comfortable operating in a small, fast-scaling team where individual contributors have meaningful influence over technical standards and delivery approaches.
• High energy with a commitment to technical quality, trusted data, client service and practice growth.