全职 Senior Full Stack Engineer (Data, AI) 工作, 薪水 up to SGD 8,500, JOBSTER PRIVATE LTD. Islandwide (Singapore) 公司招聘中 - Ricebowl

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