Data Lakehouse Architect

Responsibilities

1. Own the end-to-end architecture and technical vision for the enterprise Lakehouse platform.
2. Design data products, data marketplace, knowledge layers, and agentic AI workloads.
3. Define target architecture with focus on scalability, security, reusability, and performance.
4. Develop roadmaps, frameworks, and reusable architecture patterns for new data capabilities.
5. Design solutions using Databricks, Snowflake, Cloudera, Azure, AWS, or GCP.
6. Architect RAG, Vector DB, Graph DB, embeddings, and agentic AI workloads.
7. Partner with business teams to define data contracts, SLAs, and data quality standards.
8. Review technical/design specifications and ensure delivery follows enterprise standards.
9. Lead performance engineering, optimization, continuous improvement, and production readiness.
10. Collaborate with business, technology, vendors, and distributed engineering teams to deliver strategic solutions.

Requirements

1. 10–15 years of experience in Data Architecture / Data Engineering, preferably in FSI.
2. Strong hands-on experience designing enterprise Data Lakehouse platforms.
3. Expertise in Databricks, Snowflake, Cloudera, Azure, AWS, or GCP.
4. Strong knowledge of Iceberg, Hudi, Delta Lake, Object Storage, and Data Federation.
5. Experience with Trino, Denodo, Dremio, Hive, Impala, and distributed compute platforms.
6. Strong understanding of RAG, Vector DB, Graph DB, embeddings, and agentic workloads.
7. Experience with Kafka, Flink, Spark Streaming, APIs, real-time data, and event-driven architectures.
8. Strong knowledge of Terraform, Kubernetes/OpenShift, CI/CD, Git, Jenkins, and DevOps tools.
9. Excellent understanding of data modeling, governance, metadata, data quality, and data products.