Enterprise Data Platform Architect

Responsibilities:

• 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, materialised 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.
• Optimise 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 modernisation from legacy databases, fragmented reporting systems, and siloed data marts to a scalable enterprise data and AI-ready platform.

Skills

DWH, architect, architecture, data lake, data warehouse, warehouse