{"id":3878080,"url":"https://alion.io/job/ntt-data-senior-data-engineer-5","title":"Senior Data Engineer","company":{"id":50348,"name":"NTT DATA","domain":"nttdata.com","url":"https://alion.io/company/ntt-data","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Phenom","truth_index":{"grade":"A","score":93,"open_postings":110,"ghost_share":0,"stale_share":0.473,"repost_share":0.027,"time_to_fill_p50_days":18,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"AgentOps","optional":false},{"name":"AI Agents","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"DLP","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google ADK","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Spanner","optional":false},{"name":"GraphRAG","optional":false},{"name":"Hybrid Search","optional":false},{"name":"IAM","optional":false},{"name":"LLMOps","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"RAG","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"Fine-tuning","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Multi-Agent Systems","optional":true}],"status":"live","first_seen_at":"2026-08-20T00:00:00Z","employer_posted_date":"2026-08-20","last_verified_at":"2026-11-07T10:47:58Z","board_verified":true,"closed_at":null,"days_open":79,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":79},"description":"Role OverviewOwn 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.\nRequired Technical Skills\nGCP Data Platform: BigQuery, BigLake, Cloud Storage, Cloud Spanner, BigQuery Graph.\nData Engineering: Dataflow, Apache Beam, Pub/Sub, Dataproc, Cloud Composer, Dataform, STS.\nLakehouse: Apache Iceberg, Parquet, open table formats and cross-cloud data integration.\nGovernance & Knowledge: Dataplex, Knowledge Catalog, KAG, metadata management, ontology, lineage and data quality.\nGraph & Semantic Modeling: Cloud Spanner Graph, GQL, BigQuery Graph, graph modeling, semantic layers and reusable Measures.\nAI & GenAI: Vertex AI, Gemini, Model Garden, Vertex AI Search, Agent Runtime, ADK, A2A, MCP, and RAG / GraphRAG architectures.\nAI 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.\nNetworking & Security: VPC, Shared VPC, VPC Service Controls, Private Service Connect, Cloud NAT, IAM, Cloud KMS and Secret Manager.\nDevOps & Operations: Terraform, Cloud Build, Artifact Registry, Cloud Logging and Cloud Monitoring.\nArchitecture: Enterprise data modeling, dimensional modeling, distributed data architectures and cloud modernization.\nQualifications\n10+ years of experience in enterprise data architecture and solution design. • Minimum 3+ years of hands-on GCP architecture experience.\nProven experience designing enterprise-scale data platforms and cloud modernization solutions.\nStrong experience in data governance, graph/semantic modeling and enterprise knowledge architecture.\nDemonstrated ability to lead architecture decisions and cross-functional engineering teams.\nGoogle Professional Data Engineer or Professional Cloud Architect certification preferred.\nGood to Have\nExperience with enterprise knowledge graphs, ontology and semantic modeling. • 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.\nFamiliarity with multi-agent frameworks (ADK, LangGraph, LangChain) and agent interoperability standards such as A2A and MCP.\n• Hands-on exposure to model fine-tuning, context caching and Responsible AI / AI risk-management 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