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 Professional 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.
