Role Overview Own and drive end-to-end enterprise data architecture on Google Cloud, covering lakehouse design, data engineering, graph-based data modeling, enterprise knowledge governance and AI-ready data platforms. Lead architecture decisions across data ingestion, processing, storage, analytics, knowledge management and agentic AI capabilities. Required Technical Skills - GCP Data Platform: BigQuery, BigLake, Cloud Storage, Cloud Spanner, BigQuery Graph. - Data Engineering: Dataflow, Apache Beam, Pub/Sub, Dataproc, Cloud Composer, Dataform, STS. - Lakehouse: Apache Iceberg, Parquet, open table formats and cross-cloud data integration. - Governance & Knowledge: Dataplex, Knowledge Catalog, KAG, metadata management, ontology, lineage and data quality. - Graph & Semantic Modeling: Cloud Spanner Graph, GQL, BigQuery Graph, graph modeling, semantic layers and reusable Measures. - AI & GenAI: Vertex AI, Gemini, Model Garden, Vertex AI Search, Agent Runtime, ADK, A2A, MCP, and RAG / GraphRAG architectures. - AI Retrieval & Embeddings: Vertex AI Embeddings, BigQuery Vector Search, Vertex AI Vector Search, hybrid search, re-ranking, chunking strategies and Document AI. - AI Governance & Evaluation: Responsible AI, Model Armor, Sensitive Data Protection (DLP), Gen AI Evaluation, prompt and agent testing, observability and LLMOps / AgentOps on Vertex AI Pipelines. - Networking & Security: VPC, Shared VPC, VPC Service Controls, Private Service Connect, Cloud NAT, IAM, Cloud KMS and Secret Manager. - DevOps & Operations:



Terraform, Cloud Build, Artifact Registry, Cloud Logging and Cloud Monitoring. - Architecture: Enterprise data modeling, dimensional modeling, distributed data architectures and cloud modernization. Qualifications - 10+ years of experience in enterprise data architecture and solution design. - Minimum 3+ years of hands-on GCP architecture experience. - Proven experience designing enterprise-scale data platforms and cloud modernization solutions. - Strong experience in data governance, graph/semantic modeling and enterprise knowledge architecture. - Demonstrated ability to lead architecture decisions and cross-functional engineering teams. - Google Qualified Data Engineer or Professional Cloud Architect certification preferred. Good to Have - Experience with enterprise knowledge graphs, ontology and semantic modeling, Neo4j. - Exposure to MCP, agentic frameworks and graph-augmented retrieval. - Experience with enterprise governance, Legal, Risk and Compliance data platforms. - Experience designing secure, scalable and AI-ready enterprise data ecosystems. - Experience taking GenAI and agentic solutions from PoC to production, including evaluation frameworks and cost / latency tuning. - Familiarity with multi-agent frameworks (ADK, LangGraph, LangChain) and agent interoperability standards such as A2A and MCP. - Hands-on exposure to model fine-tuning, context caching and Responsible AI / AI risk-management frameworks.

📌 Senior Data Architect (Bengaluru)
🏢 NTT America
📍 Bengaluru