Senior Full Stack Engineer (Data, AI)

JOBSTER PRIVATE LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Jobster Private Ltd. in Singapore seeks an experienced data engineer to own end-to-end design and delivery of data pipelines, from ingestion to serving.

You will model data and build scalable storage architectures supporting operational and analytical workloads while ensuring governance and observability. You will collaborate with software engineers on platform decisions, and design systems extensible for AI/retrieval-based features; strong autonomy and communication are essential in a regulated

Qualifications

• 5–7+ years of professional software engineering experience with 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 and building production-grade backend systems.
• Solid software engineering fundamentals: API design, system architecture, full-stack capabilities.
• Experience with cloud-native data platforms or lakehouse architectures.
• Ability to operate with autonomy and lead technical decisions.
• Strong communication skills to explain trade-offs to non-technical stakeholders.

Responsibilities

• Own end-to-end design and delivery of data pipelines from ingestion to serving.
• Design data models and storage architectures for operational and analytical workloads.
• Build and maintain infrastructure for data quality, observability, and governance.
• Contribute to broader product and platform architecture with other engineers.
• Design systems extensible for AI/retrieval-based features over time.
• Collaborate with stakeholders on platform and deployment decisions.
• Operate in a regulated environment with data sensitivity considerations.

Skills

Data engineering

ETL/ELT pipelines

Data modeling

Batch and streaming processing

Backend development

Cloud platforms

Autonomy

Stakeholder communication

Tools

Databricks

Unity Catalog

Delta Lake

Job description

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