{"id":823815,"url":"https://alion.io/job/ncs-eg-senior-data-engineer","title":"#EG Senior Data Engineer","company":{"id":8907,"name":"NCS","domain":"ncs.co","url":"https://alion.io/company/ncs-co","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":82,"open_postings":376,"ghost_share":0.298,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-17T05:46:00Z"}},"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":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":50000,"max_usd":109000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":39},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"Embeddings","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tokenization","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon S3","optional":true},{"name":"AWS","optional":true},{"name":"Edge AI","optional":true},{"name":"GCP","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-08-21T17:32:02Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-17T21:17:02Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":56},"description":"NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of Industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group.\nThis is a Senior Data Engineer role within NCS AI Central’s Forward Deployed Engineering model, focused on preparing client data for production-grade AI, ML, and RAG systems.\nThe role owns the design and delivery of AI-ready data pipelines, including ingestion, cleaning, transformation, data quality checks, batch and streaming workflows, and production-scale operations. A key focus is building reliable data foundations for RAG and vector search, covering document ingestion, chunking, embeddings, hybrid search, and vector databases such as pgvector, Pinecone, Weaviate, OpenSearch, or Azure AI Search.\nYou will also handle data governance and compliance, including PII redaction, access controls, data residency, metadata, and lineage, especially for regulated sectors such as Healthcare, Government, and Transport. They will work closely with AI Engineers and AI Architects to define what “AI-ready” data means for each engagement, from fast POC/POV work through to hardened production systems.\nWhat will you do?\n1. Data Pipeline Engineering & AI-Readiness\nDesign and build ingestion, cleaning, and transformation pipelines that turn messy, real-world client data into AI-ready datasets.\nBuild batch and streaming pipelines (Airflow/Prefect/Kafka) that keep data flowing reliably into AI systems without manual intervention.\nOwn data quality - deduplication, schema validation, completeness checks - upstream of any model or RAG pipeline.\nProactively flag data gaps or quality issues that would degrade model/RAG performance downstream, before they surface as an AI Engineer's problem in testing.\n2. RAG & Vector Store Architecture\nArchitect document/data ingestion and indexing pipelines for Retrieval-Augmented Generation (RAG) systems - chunking strategy, embeddings, hybrid/vector search.\nDesign and operate vector database and search infrastructure (pgvector/Pinecone/OpenSearch) at production scale and query volume.\n3. Data Governance & Compliance\nImplement PII redaction, data residency, and access-control patterns aligned to PDPA and sector-specific requirements (Healthcare, Government, Transport).\nMaintain clear data lineage and metadata governance so engagement teams and auditors can trace how client data flows into AI outputs.\n4. FDE & Development/Maintenance Coverage\nDuring FDE engagements: rapidly assess and prepare a client's data landscape during Discover/POC, identifying data-readiness gaps early.\nDuring system development & maintenance engagements: build and operate production-scale data pipelines handling the full volume and complexity of live client systems (e.g., Healthcare or Transport data at scale).\nContribute reusable ingestion/indexing patterns back into the shared internal asset library to accelerate future engagements.\n5. Collaboration & Leadership\nPartner closely and continuously with AI Engineers and AI Architects - understanding what a given model, RAG pipeline, or agent actually needs from the data layer, and translating that into concrete pipeline and schema design decisions.\nOwn the definition of \"AI-ready\" data for each engagement jointly with AI Engineers - agreeing on chunking strategy, metadata, freshness, and quality thresholds before pipelines are built, not after retrieval quality suffers.\nSit in solution design conversations alongside AI Engineers and AI Architects, so data architecture and model/RAG architecture are designed together rather than data being treated as a downstream dependency.\nMentor junior data engineers and set data engineering standards across engagements.\nThe ideal candidate should possess:\n5+ years in data engineering, including production-scale pipeline design (not just analytics/reporting pipelines).\nStrong SQL and at least one systems language (Python/Scala/Java); hands-on with batch and streaming frameworks (Airflow, Spark, Kafka).\nExperience building data pipelines for AI/ML or RAG use cases - embeddings, vector indexing, hybrid search.\nSolid understanding of data governance, PII handling, and access-control patterns in regulated environments.\nComfortable moving between fast, exploratory data assessment (FDE/POC) and disciplined, high-volume production pipeline engineering (system development & maintenance).\nWorking understanding of core AI/LLM concepts - tokenization, embeddings, chunking strategy, context windows, RAG, and agentic workflows - sufficient to hold a real technical conversation with AI Engineers and AI Architects about what \"AI-ready\" data means for a given use case, not just how to move and clean it.\nPreferred Qualifications\nExperience with vector databases (pgvector, Pinecone, Weaviate) and search platforms (OpenSearch/Azure AI Search).\nExposure to Singapore Government data environments (GCC/HCC) and compliance regimes (IM8, PDPA).\nExperience with sector-specific data complexity - Healthcare (clinical data governance) or Transport/Aviation systems.\nFamiliarity with data cataloguing and lineage tooling.\nPrior experience embedded within an AI/ML delivery team (not just a data platform team) - i.e., has sat alongside AI Engineers day-to-day and adjusted pipeline/schema design based on model or RAG performance feedback.\nTech Stack (Illustrative)\nLanguages: Python, SQL (Scala/Java a plus)\nPipelines: Airflow/Prefect, Spark, Kafka/Debezium\nStorage/Search: Postgres, S3/Blob, pgvector/Pinecone/Weaviate, OpenSearch/Azure AI Search\nGovernance: Presidio (PII redaction), data catalogue/lineage tooling\nCloud: AWS/Azure/GCP; GCC/HCC exposure a plus\nWhy Join NCS?\nGrow with Us\nWork on cutting-edge AI products that shape the future of technology\nCollaborate with talented, passionate teams across research, engineering, and design\nAccess continuous learning opportunities and career development pathways\nMake an Impact\nTransform AI research into products that solve real problems for clients and users\nDrive innovation in a leading Technology Services Firm with regional presence\nContribute to building a better future through responsible, human-centred AI\nThrive in Our Culture\nExperience a human-to-human approach where relationships and collaboration matter\nBe part of Team NCS, where bold ideas meet practical execution\nEnjoy a supportive environment that values diversity, inclusion, and respect\nWe are driven by our AEIOU beliefs-Adventure, Excellence, Integrity, Ownership, and Unity-and we seek individuals who embody these values in both their professional and personal lives. We are committed to our Impact: Valuing our clients, Growing our people, and Creating our future.\nTogether, we make the extraordinary happen.\nLearn more about us at ncs.co and visit our LinkedIn career site.\nScam Alert\nWe are aware of fraudulent job offers and impersonations of NCS recruiters. Phishing emails using convincing-looking but fake addresses are also commonly used to trick you into thinking that they come from official NCS sources.\nPlease note that all official communications from NCS Group will only be sent from verified corporate email addresses. Always check that the sender’s email address ends with the genuine NCS domain, @ncs.com.sg and beware of extra letters, symbols or misspellings. 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