GCP Data Architect

We are looking for an experienced GCP Data Architect to lead the design and implementation of next-generation data and AI platforms on Google Cloud Platform (GCP). This role will be responsible for defining enterprise-scale data architecture, AI-ready data foundations, and modern cloud-native solutions that enable advanced analytics, Generative AI, Machine Learning, and intelligent business applications. The ideal candidate will possess deep expertise in GCP Data Services, Data Engineering, AI/ML architecture, Lakehouse platforms, and enterprise data governance, with the ability to drive architecture across multiple business domains while partnering with Engineering, Product, Analytics, and AI teams.

We are seeking a strategic technology leader who combines deep expertise in Google Cloud, Data Architecture, and Artificial Intelligence. The ideal candidate has successfully designed enterprise-scale cloud data platforms and understands how to build data foundations that power Generative AI, Machine Learning, advanced analytics, and intelligent enterprise applications. You should be equally comfortable discussing enterprise architecture with executive leadership and collaborating hands-on with engineering teams to deliver scalable, secure, AI-ready data solutions. A passion for innovation, cloud-native architecture, and leveraging AI to solve complex business problems is essential.

The candidate will have responsibilities across the following functions:

Enterprise Data and AI Architecture:

• Define and implement enterprise-wide data architecture strategy on Google Cloud Platform (GCP).
• Design scalable Lakehouse, Data Warehouse, and Data Mesh architectures supporting analytics and AI workloads.
• Create conceptual, logical, and physical data models for enterprise applications.
• Architect AI-ready data platforms capable of supporting LLMs, RAG pipelines, Vector Databases, and Agentic AI solutions.
• Establish enterprise standards for metadata, data modelling, naming conventions, security, and governance.

Google Cloud Platform (GCP) Architecture:

• Design cloud-native solutions leveraging: BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, BigLake, Cloud Composer, Vertex AI, Cloud Run, GKE, Cloud Functions.
• Optimise performance, scalability, security, and cost across GCP environments.
• Implement Infrastructure as Code (Terraform preferred).
• Design highly available, secure, and resilient cloud data platforms.

AI and Generative AI Enablement:

• Design enterprise AI data pipelines supporting: Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Semantic Search, Vector Search, AI Agents / Multi-Agent Systems.
• Build architectures that integrate structured and unstructured enterprise data for AI applications.
• Partner with AI Engineering teams to deploy scalable AI solutions using Vertex AI and modern AI frameworks.
• Enable enterprise knowledge platforms through embeddings, vector databases, and intelligent retrieval.

Data Engineering and Integration:

• Architect large-scale batch and real-time data pipelines.
• Design ETL/ELT frameworks using modern cloud-native technologies.
• Build streaming architectures using Kafka, Pub/Sub, Spark, Dataflow, or equivalent technologies.
• Ensure end-to-end data lineage, observability, monitoring, and reliability.
• Drive modernization of legacy data platforms into cloud-native architectures.

Analytics and Data Products:

• Design reusable semantic data models for BI and enterprise reporting.
• Enable self-service analytics using BigQuery and modern visualisation platforms.
• Build trusted enterprise data products for AI, Machine Learning, and business intelligence.
• Collaborate with Data Scientists and ML Engineers to support feature engineering and AI model development.

Data Governance and Security:

• Define enterprise data governance standards.
• Establish data quality frameworks and validation processes.
• Implement metadata management and data catalogue solutions.
• Ensure compliance with enterprise security, privacy, and regulatory requirements.
• Define SLAs, data contracts, and governance policies across business domains.

Leadership and Stakeholder Management:

• Serve as the technical leader for enterprise data and AI architecture.
• Collaborate with Product, Engineering, Analytics, Platform, and AI teams.
• Conduct architecture reviews and provide technical guidance across programs.
• Drive enterprise-wide adoption of modern cloud and AI best practices.
• Mentor Data Engineers, Architects, and Technical Leads.

Requirements:

• 10+ years of experience in Data Architecture, Data Engineering, Cloud Architecture, or AI Data Platforms.
• Strong expertise in Google Cloud Platform (GCP).
• Hands-on experience with: BigQuery, Dataflow, Dataproc, Pub/Sub, BigLake, Vertex AI, Cloud Composer, Cloud Storage
• Strong knowledge of distributed data processing using Apache Spark.
• Expertise in data modelling including: Relational, Dimensional (Kimball), Data Vault, Lakehouse Architecture
• Strong SQL expertise with programming experience in Python.
• Experience designing enterprise-scale ETL/ELT pipelines.
• Strong understanding of API-based and event-driven architectures.
• Experience with modern orchestration platforms including Airflow or Cloud Composer.
• Knowledge of Infrastructure as Code using Terraform.
• Experience with CI/CD pipelines for cloud data platforms.
• Excellent stakeholder management and architecture documentation skills.

Preferred Experience:

• Experience with Generative AI and Large Language Models (LLMs).
• Hands-on experience with: Vertex AI, LangChain, LangGraph, RAG Architectures, Vector Databases (Pinecone, Weaviate, Chroma, Vertex AI Vector Search, Milvus).
• Experience building enterprise AI platforms.
• Exposure to Agentic AI or Multi-Agent Systems.
• Experience with Data Mesh architecture.
• Knowledge of ML pipelines (MLOps).
• Familiarity with data governance platforms such as Collibra, DataHub, or Alation.
• Experience with BI tools including Looker, Tableau, or Power BI.
• Experience working in Agile product organisations.