Role – Enterprise Data Solution Architect
Location – Remote
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 Responsibilities
Architectural 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 Big Query, 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., Lang Chain or Llama Index)
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 Qualifications:
Core 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 Data plex for lineage, quality, and metadata management
Generative AI & Machine Learning
AI 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 LLM
MLOps: Experience building CI/CD pipelines for ML (Vertex AI Pipelines or Kubeflow)
Enterprise Architecture)
Knowledge of TOGAF or similar framework
Strong understanding of microservices architecture and API management (Apigee)
Experience & Certification
Experience: 8+ years in Data Architecture, with at least 3 years focused on GCP.
AI Background: Proven track record of deploying at least one Gen AI solution into a production environment.
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related fie
Preferred Certifications: * GCP Professional Data EngineerGCP Professional Cloud Architect
Education:
Bachelors or Masters in Information Technology, Computer Science or relevant field.

Enterprise Data Solution Architect

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