Senior Data Architect

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