Founding Data AI Engineer: Semantic Layer & LLM Apps

Newbridge; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Newbridge is building a new AI team from the ground up for a client, creating an AI-native data platform powered by Cube.js and modern semantic workflows. You will be part of the founding team defining architecture, not just maintaining it.

You will design scalable data pipelines, build LLM-powered apps, and own governance and security of the semantic layer, collaborating with AI scientists and product teams to move models from prototype to production.

Qualifications

• Experience in Data Engineering / AI Engineering with Python and SQL.
• Hands-on with Cube.js or similar semantic layer (dbt Semantic Layer, LookML, AtScale).
• Strong experience with LLMs, RAG, LangChain / LlamaIndex, Vector DBs.
• Experience with data warehouses (Snowflake / BigQuery / Redshift).
• Experience with ETL/ELT, API development and data modeling.

Responsibilities

• Design and build scalable data pipelines and semantic layer using Cube.js / Cube Cloud.
• Build LLM-powered applications on top of the semantic layer - RAG, AI agents, intelligent analytics.
• Develop and optimize data models, metrics layer and headless BI APIs.
• Integrate vector databases, LLMs and knowledge graphs for semantic search and reasoning.
• Collaborate with AI Scientists and Product to take models from prototype to production.
• Own performance, governance and security of the semantic layer.

Skills

Data Engineering / AI Engineering with

Python and SQL

Cube.js / semantic layer

LLMs / RAG / LangChain

Vector DBs

Data warehouses (Snowflake, BigQuery,)

Tools

Cube.js

dbt Semantic Layer

LookML

AtScale

Docker

Kubernetes

Job description

Newbridge is building a new AI team from the ground up for a client, creating an AI-native data platform powered by Cube.js and modern semantic workflows. You will be part of the founding team defining architecture, not just maintaining it.

You will design scalable data pipelines, build LLM-powered apps, and own governance and security of the semantic layer, collaborating with AI scientists and product teams to move models from prototype to production.