AI Engineer /Lead

Newbridge; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Newbridge is seeking an Analytics Engineer to design and own a production-grade Semantic Layer and Metrics Graphs that power BI and AI features for thousands of users. You will own the metrics store and graph layer that underpin analytics and embedded experiences.

The role requires deep expertise in Cube.js or dbt Semantic Layer, strong Graph knowledge, and hands-on data warehouse work across Snowflake, BigQuery, Databricks, or Redshift.

Qualifications

• Experience building production-grade semantic layers (Cube.js or dbt Semantic Layer) and metrics graphs.
• Strong SQL and dimensional data modeling skills.
• Experience with modern data warehouses (Snowflake, BigQuery, Databricks, Redshift).
• Knowledge of graph databases (Neo4j, Neptune, TigerGraph) or GraphQL API architecture.
• Ability to build secure, high-performance APIs for analytics.

Responsibilities

• Design, build, and own the end-to-end Semantic Layer - the single source of truth for all business metrics.
• Architect and build Graph-based models to add context, relationships, and lineage to metrics.
• Build secure, high-performance APIs for internal and embedded analytics consumption.
• Own pre-aggregation strategy, caching, and query performance tuning.
• Implement governance, security, and multi-tenant access control for analytics data.
• Partner with Data Eng, Product, and Frontend teams to deliver self-serve data products.
• Own documentation, data quality, and adoption of the semantic layer across the organization.

Skills

Semantic Layer

Graph data modeling

GraphQL API architecture

SQL

Tools

Cube.js

CubeCore

dbt Semantic Layer

dbt Cloud/Core

Snowflake

BigQuery

Databricks

Redshift

Neo4j

GraphQL API

Airflow

Kubernetes

TypeScript/Node.js

Python

Superset/Looker

Job description

Overview

Our client is building a next-generation data product platform where analytics is not an afterthought - it IS the product. We are looking for an Analytics Engineer who has built a production-grade Semantic Layer using Cube.js or dbt Semantic Layer and has strong experience working with Graph. You will own the metrics store + graph layer that powers BI, and AI-driven experiences for thousands of users.

Must-Have Skills

Must-Have Skills [Non-Negotiable]:

1. Semantic Layer Expertise - Must have ONE

• Track A - Cube.js / CubeCore: 2+ years in production building cubes, views, pre-aggregations, rollups, blending, securityContext, multi-tenancy, and Cube Store. Experience with Cube Cloud deployment on Docker/K8s.
• Track B - dbt Semantic Layer: 2+ years in production building semantic models, metrics [simple/derived/cumulative/conversion], saved queries, and exposing via GraphQL/JDBC APIs. Experience with dbt Cloud/Core.

2. Graph Expertise - Must Have

• Hands-on experience in Graph data modeling and implementation.
• Proficiency in at least one: Neo4j / Amazon Neptune / TigerGraph / Memgraph OR GraphQL API architecture
• Strong knowledge of Graph query languages: Cypher / Gremlin / GraphQL
• Experience building Knowledge Graphs, Metrics Graphs, or Property Graphs for analytics use cases
• Understanding of how to integrate graph context with semantic/metrics layer

3. Core Analytics Engineering

• Expert-level SQL and Dimensional Data Modeling [Star, Snowflake, Data Vault]
• Strong hands-on with Modern Data Warehouse: Snowflake / BigQuery / Databricks / Redshift
• Expert in dbt for transformation
• Experience building Row-Level Security, performance optimization, and caching strategies for sub-second analytics
• Experience powering BI tools or customer-facing embedded analytics [Superset][Metabase][Looker][PowerBI]

Key Responsibilities

1. Design, build and own the end-to-end Semantic Layer - the single source of truth for all business metrics.
2. Architect and build Graph-based models [Knowledge Graph] to add context, relationships, and lineage to metrics.
3. Build secure, high-performance APIs[GraphQL/REST] for internal and embedded analytics consumption.
4. Own pre-aggregation strategy, caching, and query performance tuning.
5. Implement enterprise-grade governance, security, and multi-tenant access control.
6. Partner with Data Engineering, Product, and Frontend teams to deliver self-serve data products.
7. Own documentation, data quality, and adoption of the semantic layer across the organization.

Tech Stack: Cube.js, dbt, Snowflake/BigQuery, Neo4j/GraphQL, Airflow, Kubernetes, TypeScript/Node.js, Python, Superset/Looker

Nice to Have

• Experience with BOTH Cube.js AND dbt Semantic Layer - or migration experience
• Graph + Vector DB experience for GenAI / RAG use cases
• Experience with Apollo Federation
• Prior experience in B2B SaaS building external-facing analytics
• Contributions to Cube.js / dbt open source