Partab Rai

Architected Kubernetes-native deployments for mission-critical platform components using AKS and Kubernetes with autoscaling, rolling updates, and fault-tolerant microservices. Partnered across engineering and platform teams to translate data platform requirements into secure, scalable delivery patterns. Implemented CI/CD with GitHub Actions and Azure DevOps for continuous delivery, policy validation, and zero-downtime deployments. Designed secure authentication/authorization using Keycloak (OIDC/JWT), OpenFGA (RBAC), and OPA (ABAC) for fine-grained governance. Built large-scale AWS data lakehouse patterns using Trino, Lakekeeper, and Apache Iceberg and established monitoring with Prometheus, Grafana, and Flink. Reduced QA effort by 60% using Claude/GPT test agents generating TestRail cases and unit test code; also built MCP-server agentic platforms for autonomous pipeline orchestration.

Architected a scalable Azure lakehouse integrating SAP CRM, Oracle, and MySQL using Azure Data Factory, Databricks, and Azure DevOps pipelines. Optimized ETL/ELT and improved throughput/reliability by addressing real-time feed issues via ADF, Databricks, and Azure Event Hub. Deployed and managed AKS clusters for scalable, highly available data workloads. Delivered low-code business solutions using Power Platform (Power Apps, Power Automate, Power BI, Dataverse) integrated with enterprise ERP systems. Conducted KPI creation, data profiling, cataloguing, and lineage mapping for SAP-sourced ETL/ELT; identified anomalies to prevent reporting disruptions. Assessed existing Azure data platform architecture and delivered strategic improvement plans for scalability, reliability, and governance.

Supported enterprise data and digital transformation initiatives evolving operational data capabilities for analytics and downstream applications. Managed DevOps deployments across pods for Azure Fabric services and designed/maintained enterprise ETL/ELT pipelines using ADF, Databricks, Snowflake, and Azure ADLS/OneLake. Built CI/CD using Databricks Asset Bundles (DABs), Azure DevOps, and Git for version control and zero-downtime deployment. Designed pipelines aligned to canonical data models with consistent schema definitions in Unity Catalog across airline reservation, operations, and revenue domains. Applied IATA standards to ensure interoperability and alignment with aviation/regulatory reporting needs. Implemented Databricks Mosaic AI/Genie for generative and multi-agent AI applications, and created LLM-driven analytics workflows using Ollama, LangChain, and vector databases (Pinecone/Weaviate). Delivered Spark Streaming frameworks with Python and Event Hub for real-time and batch