Senior Data Architect

VettedBench; India; Remote; INR 1,320,000 - 1,581,000; Full time

Job summary

VettedBench is seeking a Senior Data Architect to lead the design and implementation of enterprise-scale data architecture on Google Cloud Platform. You will steer data lakehouse initiatives, governance, and semantic modeling to enable AI-ready enterprise data platforms.

The role covers end-to-end architecture decisions, data security, and collaboration with cross-functional teams to modernize cloud data ecosystems for scalable AI workloads.

Qualifications

• 9–14 years of experience in data architecture or solution architecture.
• Minimum 3+ years of hands-on GCP architecture experience.
• Experience designing enterprise-scale data platforms.
• Knowledge of data lakehouse and cloud modernization.
• Experience with data governance, graph modeling and semantic architecture.
• Experience with AI-ready data platforms, RAG and vector search.
• Experience leading technical teams and architecture discussions.
• Google Professional Data Engineer or Professional Cloud Architect certification preferred.

Responsibilities

• Design and implement end-to-end enterprise data architecture on GCP.
• Lead data lakehouse architecture using BigQuery, BigLake, Iceberg and Cloud Storage.
• Architect scalable data ingestion, processing and transformation pipelines.
• Implement enterprise data governance, metadata management, data lineage and quality.

Skills

GCP Data Platform

Data Engineering

Data Lakehouse

Data Governance

Graph & Semantic Modeling

AI & Generative AI

AI Retrieval & Embeddings

AI Governance & Evaluation

Cloud Security & Networking

DevOps & Architecture

Job description

Job Description – Senior Data Architect

Experience: 9–14 Years

Location: Remote

Employment Type: Contract

Budget: Up to ₹1.3 LPM

Background Verification: Basic BGV

Job Overview

We are looking for an experienced Senior Data Architect to lead the design and implementation of enterprise-scale data architecture on Google Cloud Platform (GCP). The ideal candidate should have strong expertise in GCP data services, data lakehouse architecture, data governance, graph-based data modeling, AI retrieval, embeddings and Generative AI platforms.

The candidate will drive end-to-end data architecture decisions, build scalable and secure data ecosystems, and enable AI-ready enterprise data platforms.

Key Responsibilities

• Design and implement end-to-end enterprise data architecture using Google Cloud Platform.
• Lead data lakehouse architecture using BigQuery, BigLake, Apache Iceberg and Cloud Storage.
• Architect scalable data ingestion, processing and transformation pipelines.
• Implement enterprise data governance, metadata management, data lineage and data quality frameworks.
• Design graph-based data models, knowledge graphs, ontologies and semantic layers.
• Build AI-ready data platforms supporting RAG, GraphRAG, vector search and embedding-based retrieval.
• Leverage Vertex AI, Gemini and other Google AI services for enterprise GenAI and agentic AI solutions.
• Define cloud security, networking, IAM and data protection strategies.
• Lead architecture reviews, technical decision-making and cloud modernization initiatives.
• Collaborate with cross-functional engineering teams and business stakeholders.

Required Technical Skills

1. GCP Data Platform

• BigQuery, BigLake, Cloud Storage, Cloud Spanner and BigQuery Graph.

2. Data Engineering

• Dataflow, Apache Beam, Pub/Sub, Dataproc, Cloud Composer and Dataform.

3. Data Lakehouse

• Apache Iceberg, Parquet, open table formats and cross-cloud data integration.

4. Data Governance

• Dataplex, Knowledge Catalog, metadata management, data lineage and data quality.

5. Graph & Semantic Modeling

• Cloud Spanner Graph, GQL, graph modeling, ontologies and semantic layers.

6. AI & Generative AI

• Vertex AI, Gemini, Model Garden, Vertex AI Search, Agent Runtime, ADK, A2A and MCP.

7. AI Retrieval & Embeddings

• RAG, GraphRAG, Vertex AI Embeddings, BigQuery Vector Search, Vertex AI Vector Search, hybrid search, re-ranking and Document AI.

8. AI Governance & Evaluation

• Responsible AI, Model Armor, Sensitive Data Protection (DLP), GenAI evaluation, LLMOps and AgentOps.

9. Cloud Security & Networking

• VPC, Shared VPC, VPC Service Controls, Private Service Connect, IAM, Cloud KMS and Secret Manager.

10. DevOps & Architecture

• Terraform, Cloud Build, Artifact Registry, Cloud Logging, Cloud Monitoring, enterprise data modeling and distributed data architecture.

Required Qualifications

• 9–14 years of experience in data architecture, enterprise data platforms or solution architecture.
• Minimum 3+ years of hands-on Google Cloud Platform architecture experience.
• Proven experience designing and implementing enterprise-scale data platforms.
• Strong knowledge of data lakehouse architecture and cloud modernization.
• Experience in enterprise data governance, graph modeling and semantic architecture.
• Strong understanding of AI-ready data platforms, RAG and vector search architectures.
• Experience leading technical teams and architecture-level discussions.
• Google Professional Data Engineer or Professional Cloud Architect certification is preferred.

Good to Have

• Experience with enterprise knowledge graphs, ontology and Neo4j.
• Exposure to MCP, ADK, LangGraph and LangChain.
• Experience with Legal, Risk and Compliance data platforms.
• Hands-on experience taking GenAI and agentic AI solutions from PoC to production.
• Knowledge of model fine-tuning, context caching and Responsible AI frameworks.
• Experience with AI evaluation, observability, cost optimization and latency tuning.

Candidate Profile

The ideal candidate should be a hands-on technical architect with strong GCP expertise, capable of designing secure, scalable and AI-enabled enterprise data ecosystems while leading architecture decisions and engineering teams.

Pay: ₹130,000.00 - ₹140,000.00 per month

Work Location: Remote