Head of Engineering

Newbridge; Singapore; On-site; SGD 320,000 - 420,000; Full time

Job summary

Newbridge is a frontier AI company building a next-generation AI platform, featuring a generative AI + simulation-powered search engine. The role leads end-to-end data engineering initiatives and architects scalable data infrastructure including lakes, lakehouse, and AI-enabled analytics.

You will own data pipelines, feature stores, governance, and enable AI agents with robust data products for research, product, and commercial teams in a high-impact environment.

Qualifications

• 12-15+ years designing and building data products in AI-native environments.
• Hands-on lakehouse ecosystems with low-latency batch and streaming pipelines.
• Experience with feature stores, Spark, Python, and governance tooling.

Responsibilities

• Lead end-to-end data engineering initiatives and architecture.
• Design, build, and scale data pipelines and lakehouse architectures.
• Own data lake ecosystem, standards for ingestion, storage, and access.

Skills

Data engineering

Data architecture

Feature stores

SQL

Python

GPU compute

Tools

Spark

Ray

Dask

Databricks

Snowflake

Delta Lake

Iceberg

Job description

About Our Client:Our client is a frontier AI company building a next-generation AI platform - a generative AI + simulation-powered search engine.

The Role:Lead end-to-end delivery of data engineering initiatives. Architect and scale the core data infrastructure that powers their business - from data lakes and enterprise data platforms to AI-enabled analytics products and agentic systems. High-impact opportunity to build foundational systems that drive decision-making across research, product, and commercial teams.

Key Responsibilities

Data Infrastructure & Architecture

• Design, build, and scale data pipelines and lakehouse architectures supporting enterprise, product, and commercial analytics at scale
• Own the data lake ecosystem, defining standards for ingestion, storage, transformation, and access across structured and unstructured data
• Evolve the data stack for scalability, performance, and developer experience, optimizing for multi-cloud compute and supercomputing environments
• Build and maintain centralized feature registry / feature store as single source of truth for feature cataloging, lineage, ownership, SLAs - ensuring training/serving consistency

Data Products & Platforms

• Develop and own core data products including enterprise data platform, intelligence layer, and AI-powered analytics tools (including AI agents) for non-technical users
• Build robust data models supporting analytics, reporting, and ML across multiple business lines
• Enable Applied AI/ML Engineers (Agents) building agents that automate workflows

Governance & Data Quality

• Champion data quality, governance, observability to ensure organization-wide trust in data
• Implement lineage and auditability for training data used in generative models

Candidate Profile

• 12-15+ years designing and building data products - enterprise data platforms, analytics platforms, personalization systems in AI-native environments
• Hands-on lakehouse ecosystems, low-latency large-scale batch and streaming pipelines for ML optimized for GPU compute
• Feature stores, Spark/Ray/Dask, Databricks/Snowflake/Delta Lake/Iceberg
• Deep SQL, Spark, Python. Governance & observability tooling
• Translates complex technical concepts for product and commercial teams
• Scrappy startup experience - as technical as possible, as commercial as possible