Enterprise Data Architect

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.