Google Cloud Platform Data Architect

Detailed JD:

We are seeking a highly skilled Google Cloud Platform Data Architect to design, implement, and manage our organization's data infrastructure, with a specific focus on leveraging Google Cloud's Vertex AI Search and Agent Builder capabilities. This role will bridge the gap between data management practices and cutting-edge AI applications, enabling enhanced search experiences, RAG systems, and AI-powered recommendations to drive business growth.

Experience:

o 10+ years of industry experience designing and managing data solutions.

o Proven experience with Google Cloud Platform (Google Cloud Platform) services (BigQuery, Dataflow, Cloud Storage, etc.).

o Hands-on experience with Vertex AI, specifically implementing Vertex AI Search and Vector Search.

o Experience designing and building data architectures that support AI and ML applications.

o Proficiency in programming languages such as Python, SQL, and Java.

Key Responsibilities:

Architecture Design: Design and architect scalable, secure, and cost-effective data solutions on Google Cloud Platform (Google Cloud Platform) that support both traditional analytics and advanced AI/ML workloads.

Vertex AI Search Implementation: Lead the design and implementation of enterprise-grade search experiences using Vertex AI Search (part of Vertex AI Agent Builder) across websites, intranets, and RAG systems for generative AI applications.

Data Pipeline Development: Design and build robust, end-to-end data pipelines (ingestion, transformation, storage) that feed high-quality data to the Vertex AI systems using services like BigQuery, Cloud Storage, Dataflow, and Pub/Sub.

Generative AI Integration: Collaborate with data science and engineering teams to translate business requirements into AI-based solutions, including building and deploying generative AI models (e.g., for chatbots, content creation) within the Vertex AI framework.

Vector Search Expertise: Implement vector search and embeddings for semantic search and recommendation systems, organizing data by meaning to provide highly relevant results in milliseconds.

Data Governance & Security: Establish data policies, standards, and security protocols to ensure data accuracy, accessibility, security, and compliance with industry regulations within the Google Cloud Platform environment.

Performance Optimization: Monitor and optimize the performance of data infrastructure and AI search systems, troubleshooting complex technical issues as they arise.

Technical Leadership & Collaboration: Provide technical leadership and documentation (e.g., architecture diagrams, data models) to cross-functional teams and stakeholders, ensuring alignment between data strategies and organizational goals.

Technical Knowledge:

o Deep understanding of data modeling, data warehousing, data lakes, and ETL processes.

o Familiarity with ML operations (MLOps) practices and tools for model lifecycle management (training, evaluation, deployment, monitoring).

o Knowledge of networking, cybersecurity fundamentals, and compliance standards.