Senior Full Stack Engineer (Data, AI)
Posted Updated
Summary
Senior Full Stack Engineer owning end-to-end data pipelines—from ingestion through transformation to serving—in a regulated Singapore-based environment, with emphasis on data modeling, lakehouse architectures, and preparing infrastructure for AI/retrieval-based features.
Job Description
Key Responsibilities
. Own end-to-end design and delivery of data pipelines, from ingestion to transformation to serving
. Design data models and storage architectures that support both operational and analytical workloads
. Build and maintain infrastructure for data quality, observability, and governance
. Contribute to broader product and platform architecture, working alongside other software engineers as priorities shift
. Design systems that are extensible enough to support AI/retrieval-based features over time
. Contribute significantly to key technical decisions, escalating trade-offs where they intersect with broader priorities
. Collaborate with stakeholders on platform and deployment decisions
. Work with attention to data sensitivity and system constraints in a regulated environment
Qualifications
Technical Requirements
Required
. 5-7+ years of professional software engineering experience, with demonstrated ownership of production data systems end-to-end
. Strong data engineering fundamentals:
ETL/ELT pipeline design, data modeling, batch and streaming processing . Strong proficiency in
at least one general-purpose programming language, with a track record of building production-grade backend systems, not just data scripts or pipelines . Solid software engineering fundamentals:
API design, system architecture, ability to work across the stack when needed . Experience working with
cloud-native data platforms or lakehouse architectures . Comfortable operating with significant autonomy and taking a leading role in technical decisions . Strong communication skills able to explain technical trade-offs to non-technical stakeholders Good to have: . Experience with
Databricks, Unity Catalog, Delta Lake, or similar lakehouse tooling . Experience building data pipelines to support
retrieval-augmented generation (RAG) or other AI/ML workflows, e.g. embedding generation, vector store population . Experience in government, public sector, or other regulated environments with data sensitivity requirements . Experience with cloud-native deployment platforms
ETL/ELT pipeline design, data modeling, batch and streaming processing . Strong proficiency in
at least one general-purpose programming language, with a track record of building production-grade backend systems, not just data scripts or pipelines . Solid software engineering fundamentals:
API design, system architecture, ability to work across the stack when needed . Experience working with
cloud-native data platforms or lakehouse architectures . Comfortable operating with significant autonomy and taking a leading role in technical decisions . Strong communication skills able to explain technical trade-offs to non-technical stakeholders Good to have: . Experience with
Databricks, Unity Catalog, Delta Lake, or similar lakehouse tooling . Experience building data pipelines to support
retrieval-augmented generation (RAG) or other AI/ML workflows, e.g. embedding generation, vector store population . Experience in government, public sector, or other regulated environments with data sensitivity requirements . Experience with cloud-native deployment platforms