Domain: Wealth Management & Analytics | Azure, Databricks, Delta Lake, Python, PySpark, ADF, Kafka
- Designed and scaled enterprise data ingestion and processing platforms supporting analytics and AI consumers across UBS Wealth Management — managing platform SLAs, pipeline reliability, and cross-functional delivery across a team of 10 engineers.
- Architected and delivered the Enterprise Regulatory Data Platform on Lakehouse / Medallion architecture using Azure Databricks and Delta Lake, enabling unified regulatory and analytics data products.
- Designed and deployed multi-terabyte scale batch and real-time data pipelines on Azure and on-prem systems using Azure Data Factory, Databricks, Apache Kafka, and Airflow, enabling low-latency data processing for enterprise analytics and AI use cases.
- Implemented CI/CD pipelines for data platform deployments, reducing manual deployment effort by 80% (from 2 hours to 25 minutes) through automation using Git, GitLab CI.
- Defined SLAs/SLOs for critical data products and led incident management, root-cause analysis, and post-mortems, improving platform uptime and reducing mean time to recovery.
- Delivered GenAI integration workstreams leveraging LangChain, Azure OpenAI, RAG pipelines, and vector databases to augment data platform capabilities.
- Led technical architecture discussions with senior stakeholders, presenting architecture designs and ensuring alignment between engineering delivery and business priorities.
Key Achievement
- UBS APAC STAR Award — Delivered the One WMA project generating
