{"id":645151,"url":"https://alion.io/job/cfgi-data-ai-data-engineer","title":"Data & AI - Data Engineer","company":{"id":672933,"name":"CFGI","domain":"cfgi.com","url":"https://alion.io/company/cfgi","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":82,"ghost_share":0.22,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":555,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":113000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":50},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"Collibra","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Embeddings","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"Great Expectations","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LookML","optional":false},{"name":"Neo4j","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Weaviate","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-08-19T02:39:43Z","employer_posted_date":"2026-08-19","last_verified_at":"2026-10-28T20:12:14Z","board_verified":true,"closed_at":null,"days_open":70,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":70},"description":"Data & AI - Data Engineer\nCFGI 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.\nRoughly 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.\nThis 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.\nThe role is hybrid in Singapore, with occasional travel across Asia-Pacific for client work. Work-authorisation sponsorship may be supported where required.\nHealthcare 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.\nWhat you might expect:\nBuild and operate the data foundation that every client artificial-intelligence and analytics solution runs on.\nDesign 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.\nWork 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.\nDesign, 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.\nBuild 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.\nDesign 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.\nPrepare 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.\nPartner with the artificial-intelligence engineers on data contracts and interfaces.\nOwn 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.\nEmbed 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.\nCarry real ownership of data architecture at engagement scale and help set the data-engineering standard for the practice.\nContribute to the practice’s reusable components, including pipeline templates, accelerators and standards.\nHelp create the reusable pipelines and accelerators that allow the team to scale from bespoke builds to a repeatable delivery model.\nMentor junior data resources as the team grows.\nWhat you must have:\nApproximately 5-10 years of strong, current, hands-on data-engineering experience building and operating production data pipelines and platforms.\nExperience delivering data work end-to-end to a production standard, including designing, building and operating governed data pipelines and platforms.\nClient-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.\nWhat sets you apart:\nExpert Python and SQL skills.\nProduction experience with a transformation and modelling framework, particularly dbt, and disciplined, tested, version-controlled data transformation.\nHands-on delivery experience with Snowflake and/or Databricks.\nWorking understanding of open table and lakehouse storage formats such as Apache Iceberg and Delta Lake.\nExperience with dimensional modelling and data-vault modelling for analytics.\nProduction experience with a data-orchestration tool such as Dagster, Apache Airflow or dbt Cloud.\nExperience with batch and streaming ingestion.\nUnderstanding of the distinction between event transport, such as Apache Kafka, and stream processing, such as Apache Flink.\nExperience with change-data-capture patterns.\nExperience 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.\nAwareness of ontologies and knowledge graphs for structured knowledge representation in artificial-intelligence-enabled systems.\nFamiliarity with the Resource Description Framework data model, SPARQL, property-graph databases such as Neo4j, or comparable knowledge-architecture technologies.\nExperience with data-quality and validation frameworks such as GX Core, formerly Great Expectations, dbt tests, or comparable tools.\nExperience with master-data and entity-resolution techniques.\nExperience with data cataloguing, lineage and data-contract tooling such as Atlan, Collibra, OpenMetadata, or comparable platforms for production-grade, audit-ready data.\nHands-on delivery experience on at least one major cloud platform - Microsoft Azure, Amazon Web Services or Google Cloud Platform - and its associated data services.\nAn appreciation of infrastructure-as-code, such as Terraform, and cost-aware platform design.\nA working understanding of how data feeds artificial-intelligence systems, including embeddings and vector stores such as pgvector, Pinecone, Weaviate or Qdrant.\nExperience or working knowledge of chunking and retrieval-corpus preparation for retrieval-augmented generation.\nUnderstanding of the difference between analytics-grade and artificial-intelligence-grade data preparation.\nExperience building authoritative, lineage-traceable data foundations with clear controls, reproducibility and retained evidence.\nA practical grasp of data governance, privacy and residency across the Asia-Pacific markets the practice serves, not Singapore alone.\nWorking awareness of Singapore’s Personal Data Protection Act and how its cross-border-transfer requirements affect data architecture and design.\nWorking 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.\nThe ability to design pipelines, classification controls and residency controls that hold up across different Asia-Pacific jurisdictions.\nNice to have:\nHealthcare and life-sciences data experience involving clinical, claims or real-world-evidence data.\nExperience with the interoperability standards and clinical ontologies that structure healthcare data.\nFamiliarity with Fast Healthcare Interoperability Resources (FHIR), itself a Health Level Seven standard, and legacy Health Level Seven version 2 messaging.\nFamiliarity 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.\nExperience with the Observational Medical Outcomes Partnership (OMOP) Common Data Model for harmonising real-world evidence across sources.\nFamiliarity with Digital Imaging and Communications in Medicine (DICOM) for medical imaging.\nOffice-of-the-Chief-Financial-Officer data experience involving financial close and reporting, consolidation, controls or risk data - CFGI’s core buyer context.\nExperience building data foundations in a governed, audit-ready environment.\nExperience contributing reusable data accelerators to a growing practice.\nWho thrives here:\nA hands-on data engineer who enjoys owning the data foundation from ingestion through governed consumption.\nSomeone comfortable taking real ownership of data architecture at engagement scale rather than working only within a narrowly defined component.\nStrong engineering judgement with the ability to distinguish between approaches that work in development and those that are appropriate for production, governed client environments.\nComfortable working across ingestion, pipelines, transformation, lakehouse and warehouse design, semantic layers, data quality, governance, lineage and artificial-intelligence-ready data.\nAble to work closely with artificial-intelligence engineers, data scientists and client stakeholders to make data usable across analytics and artificial-intelligence use cases.\nStrong analytical and problem-solving skills with the ability to explain design choices and trade-offs clearly.\nEntrepreneurial, self-motivated, adaptable, ethical and dependable.\nComfortable operating in a small, fast-scaling team where individual contributors have meaningful influence over technical standards and delivery approaches.\nHigh energy with a commitment to technical quality, trusted data, client service and practice growth.\nTake ownership of your career at CFGI:\nJoin the founding Data & AI build team in Singapore and help build the data foundation of the practice from the ground up.\nWork directly with the Partner, Data & AI Innovation and alongside artificial-intelligence engineers and data scientists, with an artificial-intelligence architect joining as the team grows.\nWork within an operating model designed to build and operate client solutions end to end: you own the data foundation, the artificial-intelligence engineers build and run the models and agents on top of it, and the data scientists bring the modelling and analytics depth.\nHave unusual influence over how the practice’s data architecture, standards and accelerators are established because the practice is being built now.\nWork across sectors and the full lifecycle - from raw client data through governed pipelines to artificial-intelligence-ready data serving live solutions.\nWork vendor-agnostically and without audit-independence restrictions.\nBuild reusable pipelines, templates, standards and accelerators that allow the team to move from bespoke client builds to a repeatable delivery model.\nDevelop a direct path toward lead data-engineering opportunities as CFGI’s Asia-Pacific practice scales.\nHelp set the data-engineering standard early and position yourself for future senior and lead appointments within the practice.\nCFGI is an equal-opportunity employer and selects candidates based on job-related capability without regard to nationality, ethnicity, race, re...","description_format":"text","description_chars":12429,"description_truncated":true,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[{"name":"Singapore","iso":"SG","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Financial Advisory, Valuation & Restructuring","Accounting & Bookkeeping Services","Tax Advisory & Preparation"],"lifecycle":[{"event":"open","at":"2026-09-10T20:12:21Z"}],"visa":[],"liveness":{"score":69,"band":"ok","label":"Likely open","p_open":1,"p_active":0.86,"p_room":0.8,"age_days":70,"expected_fill_days":555,"reasons":["conf:3","win:early","crowd:"],"computed_at":"2026-10-28T05:59:10Z"},"pay":null,"html_url":"https://alion.io/job/cfgi-data-ai-data-engineer","json_url":"https://alion.io/job/cfgi-data-ai-data-engineer.json","meta":{"generated_at":"2026-10-28T22:59:59Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":31405,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-29T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":672933},"rest":"https://alion.io/mcp/rest/get_company?id=672933"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcfgi-data-ai-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcfgi-data-ai-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcfgi-data-ai-data-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/cfgi-data-ai-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcfgi-data-ai-data-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":645151}},{"tool":"search_jobs","arguments":{"company":"cfgi.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":645151},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}