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21 days ago
Requirements and responsibilities
Role Overview: We are seeking a visionary Enterprise Data Solution Architect to bridge the gap between complex data engineering and the frontier of Generative AI. In this role, you will design and oversee the implementation of large-scale, secure, and governed data ecosystems on Google Cloud Platform (GCP).You won’t just be moving data; you will be architecting the foundation for our next generation of AI-driven products, leveraging LLMs, RAG (Retrieval-Augmented Generation) patterns, and enterprise-grade MLOps.Key ResponsibilitiesArchitectural Strategy: Design end-to-end enterprise data architectures that support both traditional analytics and modern Gen AI workloads.GCP Ecosystem Leadership: Build scalable solutions using BigQuery, Dataflow, Dataproc, and Cloud Spanner, ensuring optimal performance and cost-efficiency.Gen AI Integration: Implement production-ready Gen AI frameworks using Vertex AI, Model Garden, and Vector Search. Design orchestration layers for LLMs (e.g., LangChain or LlamaIndex).Data Governance & Security: Enforce rigorous data privacy standards, VPC Service Controls, and IAM policies, especially concerning the ingestion of proprietary data into AI models.Modern Data Modeling: Oversee the transition from legacy silos to modern architectures like Data Mesh or Data Lakehouse.Stakeholder Collaboration: Act as the technical liaison between C-suite executives, data scientists, and DevOps teams to ensure business alignment.Technical QualificationsCore Data Engineering (GCP Focus)Expertise: BigQuery (ML, Omni, BigLake), Pub/Sub, Cloud Storage, and Dataform/dbt.Pipeline Mastery: Advanced experience in Python, Java, or Go for complex ETL/ELT development.Governance: Proficiency in Google Cloud Dataplex for lineage, quality, and metadata management.Generative AI & Machine LearningAI Frameworks: Hands-on experience with Vertex AI (Foundational Models, Search, and Conversation).Architectural Patterns: Deep understanding of Vector Databases, embeddings, and fine-tuning strategies for LLMs.MLOps: Experience building CI/CD pipelines for ML (Vertex AI Pipelines or Kubeflow).Enterprise ArchitectureKnowledge of TOGAF or similar frameworks.Strong understanding of microservices architecture and API management (Apigee).