Enterprise Data Platform Architect

Role Summary

We are looking for a senior Enterprise Data Platform Architect to design and govern end-to-end data architecture across transactional systems, data warehouse, data lake, real-time pipelines, analytical platforms, and AI-enabled data platforms.

The candidate should have strong hands-on experience with PostgreSQL, MongoDB, Redis, Kafka, Elasticsearch, ClickHouse, 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.

Key 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, 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.

Required Skills
- Strong experience in enterprise data architecture, data warehouse, data lake, OLTP modeling, analytical platforms, and AI-ready data architecture.
- Hands-on experience with PostgreSQL, MongoDB, Redis, Kafka, Elasticsearch, ClickHouse.
- Solid SQL, Python, ETL/ELT, data modeling, and performance tuning skills.
- Strong understanding of OLAP modeling: facts, dimensions, aggregates, semantic layers, historical data, data marts, and analytical query optimization.
- Good understanding of OLTP modeling: normalization, transactions, indexing, concurrency, partitioning, replication, HA, and query optimization.
- Experience with Kafka topics, partitions, retention, consumer lag, schema evolution, CDC, replay, and event-driven architecture.
- Working knowledge of AI/GenAI data patterns including RAG, embeddings, vector search, semantic search, prompt/context data preparation, and AI agent data access.
- Understanding of MCP concepts such as exposing enterprise data sources, tools, schemas, documents,



and APIs securely to LLM-based applications.
- Experience in data quality, reconciliation, metadata, lineage, monitoring, alerting, and production troubleshooting.
- Good understanding of cloud infrastructure, Linux, storage, networking, DevOps, CI/CD, automation, and platform observability.

Preferred Skills
- Airflow, Debezium, Kafka Connect, Spark/Flink, Grafana, Prometheus, CloudWatch.
- Iceberg, Hudi, Delta Lake, object-storage-based data lake architecture.
- pgvector, MongoDB Vector Search, Elasticsearch vector search, Milvus, Weaviate, Pinecone, Qdrant, or similar vector platforms.
- Exposure to LLM applications, AI copilots, enterprise search, knowledge assistants, RAG pipelines, and MCP servers.
- Experience in wealth management, broking, trading, capital markets, banking, fintech, or financial services.

Experience Required
- 1015 years of experience in data architecture, database engineering, data engineering, data warehouse, data lake, DBA, or platform architecture.
- Minimum 5+ years in a senior architecture role managing enterprise-scale transactional and analytical data platforms.
- Exposure to AI-ready data platforms, semantic search, vector search, or enterprise GenAI use cases is preferred.

Ideal Candidate Profile The ideal candidate should be a hands-on Enterprise Data Platform Architect who can balance Data Warehouse, Data Lake, OLTP architecture, and AI-ready data platform design. They should understand how transactional systems are modeled, how data flows through Kafka, CDC, APIs, files, and ETL pipelines, how it lands in the warehouse or lake, how it is consumed by dashboards and reports, and how it can be safely exposed to AI assistants, RAG pipelines, vector databases, and MCP-based tools.

They should be comfortable discussing data warehouse modeling, data lake architecture, ClickHouse analytics, Kafka streaming, PostgreSQL performance, MongoDB modeling, Redis caching, Elasticsearch indexing, vector search, RAG, MCP, data quality, reconciliation, HA/DR, security, compliance, and observability.

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