Role & responsibilities:

- The candidate should have strong 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.