Enterprise Data Architect ($80-100k + bonus)

The company is a leading enterprise with extensive AI capabilities to drive GenAI transformation. They are seeking an Enterprise Data Architect to lead enterprise data platform, lakehouse, governance, and BI architecture initiatives.

Responsibilities

Lead end-to-end architecture design for enterprise data platforms, including data lakehouse, real-time data pipelines, and AI-driven analytics ecosystems

Design and implement Agentic AI frameworks and GenAI solutions (e.g., RAG, intelligent agents, self-service analytics) using Python and modern orchestration frameworks

Define enterprise data architecture standards, including data modelling, governance, data quality, and security across multi-cloud environments

Architect and scale MLOps and AI lifecycle frameworks, enabling efficient development, deployment, and monitoring of machine learning and AI solutions

Drive data platform modernization, migrating legacy systems to cloud-native architectures (AWS, Azure, GCP, Databricks)

Collaborate with business stakeholders to translate requirements into scalable, value-driven data and AI solutions

Lead and mentor cross-functional teams, enforcing architecture governance, CI/CD, and best practices across data and AI initiatives

Partner with engineering teams to enable self-service analytics and enterprise intelligence capabilities

Requirements

Bachelor's Degree in Computer Science or in relevant field

8–12+ years of experience in data architecture, solution design, and enterprise data platforms from scalable enterprise/banks/insurers/Big 4

Proven experience designing data lakehouse, distributed data systems, and large-scale data platforms

Strong hands-on expertise in Python, SQL, and data engineering frameworks

Demonstrated experience in Agentic AI / GenAI solutions, including LLM integration, RAG architectures, or AI agent frameworks

Solid experience in MLOps, AI lifecycle management, and model deployment at scale

Deep knowledge of multi-cloud environments (AWS, Azure, GCP) and modern data platforms (e.g., Databricks, Snowflake)

Strong understanding of API-driven architectures, microservices, and event-driven systems

Experience driving data governance, data quality, and enterprise data strategy

Proven leadership experience managing teams and delivering complex, cross-functional data initiatives

Strong communication skills with the ability to bridge business strategy and advanced technology solutions

Excellent commands in both Cantonese and English; Mandarin is desired.