Kundan – Python, Azure DevOps, Data Modeling

A financial data platform for reference-data management, instrument mastering, market data processing, and derivative relationships. The project involved re-architecting a monolithic system using Microsoft Fabric, Lakehouse, Cosmos DB, and Eventhouse, with API-driven services for instrument mastering, quote collection, and external data feed integration.

• Led the design and delivery of enterprise-scale data platforms using Microsoft Fabric, implementing OneLake, Lakehouse, and Medallion architectures;
• Architected and modernized data ingestion and ETL pipelines using Fabric Data Pipelines, Dataflows Gen2, Spark, Azure Data Factory, and Databricks;
• Developed semantic models and Power BI dashboards supporting executive reporting and self-service analytics;
• Implemented CI/CD automation using Azure DevOps for DEV–UAT–PROD deployments;
• Optimized Spark workloads and ETL processes to improve performance, scalability, and cost efficiency. Built an end-to-end data platform and supporting framework;
• Implemented file- and record-level traceability and configurable data-loading workflows;
• Developed shared data normalization and quality-control libraries with Pytest coverage;
• Developed AI-assisted tools for development, QA, normalization, and code review;
• Integrated the development workflow with GitLab CI/CD.

A cloud data platform modernization project focused on migrating legacy systems, adopting Lakehouse architecture, and modernizing batch and real-time data processing to support advanced analytics. The solution included CDC and streaming pipelines for low-latency, reliable data ingestion across transactional, analytical, and event-driven systems. The project also introduced data governance, security, and access controls using Unity Catalog and cloud-native capabilities, while providing architecture guidance, platform roadmaps, and technical mentoring to support the adoption of modern data engineering practices.

• Architected cloud-native data platforms using Microsoft Fabric, Databricks, Synapse, Snowflake, dbt, and Airflow;
• Led enterprise data platform modernization and legacy-to-cloud migration initiatives;
• Designed and implemented CDC and real-time streaming pipelines for low-latency analytics;
• Established data governance, security, and access control frameworks;
• Acted as a trusted advisor, defining architecture roadmaps and best practices for engineering teams.

A large-scale cloud data platform focused on data ingestion, transformation, analytics, and business intelligence. The project involved building batch and real-time data pipelines using Databricks and Snowflake, developing BI-ready data models and dashboards, and implementing Git-based CI/CD automation for scalable and reliable deployments. The solution also covered cloud data migrations, high-level architecture design, performance optimization, and improvements to platform scalability, query efficiency, and overall reliability.

• Designed and delivered large-scale data ingestion and analytics platforms using Databricks and Snowflake;
• Built batch and real-time data pipelines supporting enterprise reporting and analytics use cases;
• Developed BI-ready data models and dashboards for business and technical stakeholders;
• Implemented CI/CD automation and Git-based deployment workflows;
• Led cloud migration and performance optimization initiatives.

A financial data and reporting platform supporting banking transactions, accounts payable and receivable, and financial analytics. The project focused on building and maintaining SSIS-based ETL solutions, SQL Server data marts, and scalable workflows for financial reporting and regulatory use cases. The solution included SSRS dashboards and scheduled reports, automated reconciliation and data validation, as well as exception-handling mechanisms to improve data integrity, auditability, and operational reliability.

• Designed and maintained SSIS-based ETL applications supporting financial and operational analytics;
• Built and optimized SQL Server data marts and ETL workflows.
• Developed SSRS reports and dashboards for finance and operations teams;
• Implemented data validation, reconciliation, and exception-handling frameworks;
• Improved ETL performance and reporting SLAs through tuning and optimization.

A large-scale enterprise data modernization project focused on migrating data warehouse platforms to Azure Synapse and modernizing legacy ETL workloads using Azure Data Factory and Databricks. The project included fraud detection, campaign analytics, and Customer 360 platforms, supporting advanced analytics and data-driven decision-making. The solution provided batch and real-time data pipelines, regulatory and executive reporting, and governed data models for business and risk use cases. It also involved close collaboration with business, compliance, and engineering teams to deliver scalable and reliable data solutions.

• Led EDW modernization, migrating legacy platforms to Azure Synapse and Databricks;
• Designed Customer 360, fraud detection, and campaign analytics platforms;
• Built real-time and batch data pipelines for operational and analytical use cases;
• Delivered regulatory and executive dashboards with high accuracy and compliance;
• Partnered with business and compliance teams to deliver governed analytics solutions.

A data platform project focused on building reliable data models, ETL processes, and BI solutions to support operational and analytical needs. The project included designing star and snowflake schemas, implementing slowly changing dimensions (SCD), and developing SSIS-based ETL pipelines for data ingestion, transformation, and integration. The solution also covered data governance, performance optimization, and migration initiatives, improving scalability, reporting accuracy, query efficiency, and compliance. Close collaboration with cross-functional teams ensured business requirements were translated into governed, high-performing data platforms and reporting solutions.

• Led and mentored data engineering teams delivering enterprise data warehouse solutions;
• Designed star and snowflake schemas and implemented SCD frameworks;
• Architected and maintained SSIS-based ETL pipelines;
• Owned data governance, performance tuning, and migration initiatives;
• Translated business requirements into scalable, high-performing data platforms.