{"id":3094633,"url":"https://alion.io/job/nuvamawealth-enterprise-data-architect","title":"Enterprise Data Architect","company":{"id":3260265,"name":"Nuvamawealth","domain":"nuvamawealth.com","url":"https://alion.io/company/nuvamawealth","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":29000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":17},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic 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curated, trusted, data mart, semantic, and consumption layers.\nDefine standards for facts, dimensions, aggregates, materialised views, semantic layers, partitions, historical data, and analytical data marts.\nDesign OLTP data models for high-volume applications such as trading, CRM, account opening, client platforms, partner platforms, and operations.\nReview transactional schema design, indexing, partitioning, archival, retention, and data access patterns across PostgreSQL and MongoDB.\nArchitect Kafka, CDC, ETL, and ELT pipelines for batch, near real-time, and event-driven data movement.\nEnsure integration between PostgreSQL, MongoDB, Redis, Elasticsearch, Kafka, ClickHouse, Data Warehouse, and Data Lake platforms.\nDefine AI-ready data architecture for RAG, semantic search, embeddings, vector stores, enterprise knowledge access, and AI agent consumption.\nGuide architecture for vector databases/vector search using platforms such as PostgreSQL pgvector, MongoDB Vector Search, Elasticsearch vector search, or similar tools.\nDesign secure MCP-based integration patterns to expose enterprise data, APIs, metadata, documents, and tools to AI assistants and agentic workflows.\nOwn data quality, reconciliation, metadata, lineage, data freshness, and source-to-target control frameworks.\nOptimise analytical workloads across ClickHouse, warehouse queries, pipelines, dashboards, reporting layers, and AI retrieval workloads.\nSupport OLTP performance engineering across PostgreSQL, MongoDB, Redis, Elasticsearch, and high-concurrency application workloads.\nDefine security, access control, masking, audit logging, retention, compliance, HA, DR, backup, restore, observability, and capacity planning standards.\nDrive modernisation from legacy databases, fragmented reporting systems, and siloed data marts to a scalable enterprise data and AI-ready platform.\nSkills\narchitect, architecture, cdc, clickhouse, data lake, data warehouse, dwh, elt, etl, 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