AI Engineer /Lead

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

Job summary

Newbridge is seeking an Analytics Engineer to craft a production-grade Semantic Layer that serves as the single source of truth for analytics across the platform. You will design Graph-based models (Knowledge Graph) and expose them via GraphQL/REST APIs, powering BI and AI-driven experiences for thousands of users while ensuring governance, security, and high performance.

Deep collaboration with Data Eng, Product, and Frontend teams will be essential to deliver self-serve data products and

Qualifications

• Semantic Layer experience in production environments (Cube.js or dbt Semantic flow)
• Graph data modeling and exposure via GraphQL/REST APIs
• Advanced SQL and dimensional modeling with modern data warehouses

Responsibilities

• Design and own the end-to-end Semantic Layer as source of truth.
• Architect Graph-based Knowledge Graph models for context and lineage to metrics.
• Build secure, high-performance GraphQL/REST APIs for analytics.
• Own pre-aggregation strategy, caching, and query tuning.
• Implement governance and multi-tenant access controls.
• Collaborate with Data Eng, Product, and Frontend to deliver self-serve data products.
• Maintain documentation and data quality for the semantic layer.

Skills

Cube.js / CubeCore

dbt Semantic flow

Graph data modeling

Neo4j / Neptune / TigerGraph / Memgram

GraphQL API

Knowledge Graphs

Graph integration with semantic layer

SQL

Dimensional modeling

Modern Data Warehouse

dbt transformations

Row-Level Security

BI integration (Superset Looker PowerB

Tools

Snowflake

BigQuery

Databricks

Redshift

Job description

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 flow 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 [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.
• OR Track B - dbt Semantic flow: 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