Senior Data Engineer/Data Engineer

Job Description This role sits within NCS AI Central's (AIC) Forward Deployed Engineering (FDE) model - the combined capability that takes AI solutions from proof-of-concept through to hardened production systems. You will operate across both fast-moving FDE engagements (POC/POV, pilot deployments for strategic and lighthouse clients) and steady-state system development and maintenance work - bringing the same rigor and a reusable, asset-fed approach to both. 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. Qualifications The ideal candidate should possess: . 10+ 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 GCC/HCC exposure a plus