Poonkgodi Senthilkumar

Project: Cloud Native Analytics Platform for Content Enablement Developing a Cloud based analytics and reporting platform enabling near real time insights into content usage, customer engagement, and sales performance across enterprise clients. • Designed and implemented scalable data processing solutions using Azure Databricks, PySpark , and Apache Spark to support large-scale analytics workloads. • Built and managed Azure Data Lake Storage (ADLS Gen2)–based architectures, applying medallion (Bronze/Silver/Gold) design patterns. • Collaborated with data architects, analysts, and engineering teams to design and optimize end-to-end Azure data platform architectures. • Implemented efficient data ingestion and transformation processes using Databricks notebooks, workflows, and job scheduling. • Apply data quality frameworks including validation rules, anomaly detection, and reconciliation checks across ingestion and transformation layers. • Optimized Spark jobs through partitioning, caching, broadcast joins, and cluster tuning to improve performance and reduce costs. • Monitored Databricks jobs and clusters using Azure Monitor, Log Analytics, and Databricks metrics to ensure pipeline reliability and performance. • Enforced data security and governance using Azure Active Directory, RBAC, Unity Catalog, and encryption at rest and in transit. • Authored and maintained comprehensive technical documentation covering Databricks workflows, data models, and operational procedures. • I have strong experience managing stakeholders through clear communication, effective negotiation, strategic thinking, influencing skills, solid technical knowledge and good decision-making.

Led the modernisation of an enterprise data warehouse to support advanced analytics, operational reporting, and business intelligence across multiple business units. • Designed and implemented end to end data ingestion and transformation pipelines using Azure Data Factory, Azure Data Lake, and Azure Synapse Analytics. • Developed scalable Spark based transformation logic using Azure Databricks and PySpark for large and complex datasets. • Migrated legacy on premise ETL workflows to Azure cloud platforms, improving scalability, reliability, and data availability. • Designed and maintained dimensional and fact based data models to support enterprise wide analytics and reporting. • Implemented data validation, reconciliation, and audit controls to ensure accuracy, completeness, and consistency across data layers. • Optimised SQL queries, Synapse workloads, and Databricks jobs to improve processing performance. • Enabled curated datasets for Power BI dashboards and self service analytics. • Automated deployment, monitoring, and scheduling using Azure DevOps CI/CD pipelines. • Provided production support and troubleshooting while meeting defined SLAs.

• Designed cloud ready ETL pipelines using Python, SQL, and Spark to process high volume banking and asset servicing data. • Supported migration of selected workloads from on premise systems to cloud based storage and compute platforms. • Built reusable and modular transformation frameworks to standardise processing across custody, fund accounting, and investment data domains. • Enhanced enterprise data warehouse schemas to support analytics for trade processing, asset valuation, and client reporting. • Implemented data validation, reconciliation, and audit checks to ensure regulatory compliance and data accuracy. • Delivered analytics ready datasets to support business intelligence and regulatory reporting initiatives.

• Developed and maintained ETL workflows using Informatica and SSIS for core banking, custody, and risk management systems. • Integrated data from transactional systems, reference data platforms, and third party financial feeds. • Implemented complex data transformation, enrichment, and standardisation logic to meet regulatory and management reporting requirements. • Loaded curated datasets into SQL Server data warehouses and data marts for risk analytics and compliance reporting. • Performed ETL performance tuning to meet strict batch processing SLAs. • Conducted data reconciliation and validation in collaboration with QA and business teams.

Project: iWatch – Next Generation Case Management System • Contributed to the development of AML, fraud detection, and regulatory compliance solutions for global money transfer operations. • Implemented online case creation and transaction monitoring workflows using MSMQ and integrated with TIBCO systems. • Led initiatives to track multiple transaction attempts and identify suspicious activities for US regulatory reporting. • Delivered regulatory reporting solutions supporting large scale consumer transaction submissions. • Developed PCI compliant components to securely mask sensitive credit card data. • Supported KYC and KYC PEP initiatives for customer verification and compliance. • Performed unit testing, system integration, deployments, and ongoing production support. Technologies: C#.NET, ASP.NET, WCF, Entity Framework, ADO.NET, SQL Server, SSIS, WPF, LINQ, DB2, XML, SOAP UI, LDAP, Okta, API Security