Role & responsibilities:

- The candidate should have solid hands-on experience with PostgreSQL, ClickHouse/Redshift, Airflow, Data Warehouse, Data Lake, ETL/ELT, data modeling, and working knowledge of AI data architecture, vector databases, RAG, embeddings, AI agents,
- and MCP-based integrations.
- The role requires the ability to design reliable OLTP data models, scalable analytical
- platforms, trusted enterprise data layers, and secure AI-ready data access patterns.
- Design and govern enterprise data architecture across OLTP systems, Data
- Warehouse, Data Lake, ClickHouse, Kafka, and reporting platforms. Own Data Warehouse and Data Lake architecture including raw, curated, trusted,
- data mart, semantic, and consumption layers.Define standards for facts, dimensions, aggregates, materialized views, semantic
- layers, partitions, historical data, and analytical data marts. Design OLTP data models for high-volume applications such as trading, CRM, account
- opening, client platforms, partner platforms, and operations. Review transactional schema design, indexing, partitioning, archival, retention, and
- data access patterns across PostgreSQL and MongoDB.
- Architect Kafka, CDC, ETL, and ELT pipelines for batch, near real-time, and event-
- driven data movement.
- Ensure integration between PostgreSQL, MongoDB, Redis, Elasticsearch,



Kafka,
- ClickHouse, Data Warehouse, and Data Lake platforms. Define AI-ready data architecture for RAG, semantic search, embeddings, vector
- stores, enterprise knowledge access, and AI agent consumption. Guide architecture for vector databases / vector search using platforms such as
- PostgreSQL pgvector, MongoDB Vector Search, Elasticsearch vector search, or similar
- tools.
- Design secure MCP-based integration patterns to expose enterprise data, APIs,
- metadata, documents, and tools to AI assistants and agentic workflows.
- Own data quality, reconciliation, metadata, lineage, data freshness, and source-to-
- target control frameworks.
- Optimize analytical workloads across ClickHouse, warehouse queries, pipelines,
- dashboards, reporting layers, and AI retrieval workloads. Support OLTP performance engineering across PostgreSQL, MongoDB, Redis,
- Elasticsearch, and high-concurrency application workloads. Define security, access control, masking, audit logging, retention, compliance, HA,
- DR, backup, restore, observability, and capacity planning standards. Drive modernization from legacy databases, fragmented reporting systems, and
- siloed data marts to a scalable enterprise data and AI-ready platform.

📌 Enterprise Data Architect (Mumbai)
🏢 Uberlife Consulting
📍 Mumbai