#EG Senior Data Engineer

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. This 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. The 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 . You 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. What will you do? 1. Data Pipeline Engineering & AI-Readiness • Design and build ingestion, cleaning, and transformation pipelines that turn messy, real-world client data into AI-ready datasets. • Build batch and streaming pipelines (Airflow/Prefect/Kafka) that keep data flowing reliably into AI systems without manual intervention. • Own data quality — deduplication, schema validation, completeness checks — upstream of any model or RAG pipeline. • Proactively flag data gaps or quality issues that would degrade model/RAG performance downstream, before they surface as an AI Engineer's problem in testing. 2. RAG & Vector Store Architecture • Architect document/data ingestion and indexing pipelines for Retrieval-Augmented Generation (RAG) systems — chunking strategy, embeddings, hybrid/vector search. • Design and operate vector database and search infrastructure (pgvector/Pinecone/OpenSearch) at production scale and query volume. 3. Data Governance & Compliance • Implement PII redaction, data residency, and access-control patterns aligned to PDPA and sector-specific requirements (Healthcare, Government, Transport). • Maintain clear data lineage and metadata governance so engagement teams and auditors can trace how client data flows into AI outputs. 4. FDE & Development/Maintenance Coverage • During FDE engagements: rapidly assess and prepare a client's data landscape during Discover/POC, identifying data-readiness gaps early. • During 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). • Contribute reusable ingestion/indexing patterns back into the shared internal asset library to accelerate future engagements. 5. Collaboration & Leadership • Partner 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. • Own 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. • Sit 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. • Mentor junior data engineers and set data engineering standards across engagements. The ideal candidate should possess: • 5+ years in data engineering, including production-scale pipeline design (not just analytics/reporting pipelines). • Strong SQL and at least one systems language (Python/Scala/Java); hands-on with batch and streaming frameworks (Airflow, Spark, Kafka). • Experience building data pipelines for AI/ML or RAG use cases — embeddings, vector indexing, hybrid search. • Solid understanding of data governance, PII handling, and access-control patterns in regulated environments. • Comfortable moving between fast, exploratory data assessment (FDE/POC) and disciplined, high-volume production pipeline engineering (system development & maintenance). • Working 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. Preferred Qualifications • Experience with vector databases (pgvector, Pinecone, Weaviate) and search platforms (OpenSearch/Azure AI Search). • Exposure to Singapore Government data environments (GCC/HCC) and compliance regimes (IM8, PDPA). • Experience with sector-specific data complexity — Healthcare (clinical data governance) or Transport/Aviation systems. • Familiarity with data cataloguing and lineage tooling. • Prior 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. Tech Stack (Illustrative) • Languages: Python, SQL (Scala/Java a plus) • Pipelines: Airflow/Prefect, Spark, Kafka/Debezium • Storage/Search: Postgres, S3/Blob, pgvector/Pinecone/Weaviate, OpenSearch/Azure AI Search • Governance: Presidio (PII redaction), data catalogue/lineage tooling • Cloud: AWS/Azure/GCP;…