- Spearheaded the migration of an extensive Alteryx workflow into Snowflake, transforming complex data processing logic into optimized SQL scripts to enhance query performance and system efficiency.- Collaborated with the Alteryx team to meticulously document and map existing workflows, ensuring accurate replication and integration within the Snowflake environment.- Led the integration of Talend to manage data flow between Snowflake and Power BI, establishing a robust pipeline for real-time data analytics and developed and optimized interactive dashboards in Power BI- Led the strategic planning and execution of a comprehensive cloud migration, integrating Snowflake data warehousing solutions with AWS cloud services, enhancing scalability and data analytics capabilities.- Orchestrated a detailed initial assessment and migration strategy that minimized downtime and prioritized data integrity, leading to the seamless transition of legacy systems to a modern cloud-based infrastructure.- Spearheaded the design and development of a scalable real-time data processing platform, to process high-volume data streams with low latency, resulting in faster decision-making and enhanced customer experiences.- Implemented CI/CD pipelines for code deployment and testing, optimizing Spark streaming jobs and partitioning strategies to efficiently handle high throughput, resulting in improved operational efficiency.- Spearheaded the creation of a centralized data lake, enhancing data accessibility and analysis capabilities across the organization, leveraging technologies such as HDFS, SQOOP, HBASE, and AWS services- Successfully integrated multiple data sources into a unified BI, ensuring data consistency and integrity. Utilized SQOOP and custom Python scripts for efficient data ingestion, accommodating a diverse range of data formats and RDBMS systems

- Successfully migrated sensitive patient data from legacy SQL Server databases to Hadoop Distributed File System (HDFS), ensuring data integrity and compliance with healthcare regulations- Designed and optimized Spark RDD transformations using Scala to process large datasets, enabling deep insights into patient data and healthcare trends.- Designed and implemented secure, RESTful APIs using the Play Framework to facilitate seamless data exchange across various healthcare applications, ensuring compatibility and ease of integration- Spearheaded the integration and consolidation of disparate data sources into a unified analytics platform using Databricks, Delta Lake, Sqoop, and Apache Kafka, enabling a leading healthcare analytics firm to enhance data management and analytical capabilities.- Automated data import/export processes, integrating large volumes of data from various healthcare systems, which led to a 50% reduction in data processing time and supported the firm's real-time analytics and predictive modeling needs.- Configured and optimized Databricks workspaces and notebooks, employing Delta Lake for efficient data storage, which improved data query performance by 40% and facilitated advanced analytics operations.- Developed custom scripts and configurations to enhance data quality and compliance within the Delta Lake framework, leveraging its version control capabilities to maintain historical data accuracy and integrity

- Managed the integration of multi-source data systems into a unified data lake, improving data accessibility and integrity for real-time analytics across various departments- Developed and implemented data processing workflows using Apache Spark and Apache Airflow, which processed over 1 TB of data monthly, enabling timely insights and reporting- Developed secure data ingestion pipelines with Apache Nifi, standardizing data flows from diverse sources into a centralized data lake- Configured and maintained AWS S3 and Azure Data Lake storage solutions, optimizing for cost-efficiency and data retrievability- Crafted dynamic data visualization tools and dashboards using Power BI and Apache Zeppelin, directly supporting strategic decision-making processes- Implemented comprehensive data security measures, integrating AWS IAM and Azure Active Directory to safeguard sensitive information in compliance with state and federal regulations- Orchestrated data workflow automation using Apache Airflow, enhancing data pipeline efficiency and reducing manual processing time

- Involved in transition of Hive/SQL and Teradata to a Spark-based platform, reducing query processing time by over 40% and enhancing real-time analytics capabilities- Established a rigorous benchmarking protocol that proved Spark’s performance superiority, achieving a 50% increase in processing efficiency compared to the old system- Successfully converted complex Hive/SQL queries into Spark transformations, ensuring zero loss in data fidelity and significantly enhancing scalability and maintainability of analytics operation- Developed multiple proofs of concept (POCs) on a Yarn cluster, which led to optimized Spark configurations that handled increasing data volumes without performance degradation, prepared for future data growth- Engineered and implemented robust data ingestion solutions that increased data throughput by 40%, ensuring efficient handling of diverse and high-volume data sources- Seamlessly integrated advanced analytics tools (Tableau, QlikView, SAS) with Hadoop, enabling real-time data analysis and supporting dynamic BI team decision-making- Executed comprehensive system optimizations, including performance tuning and security enhancements, which resulted in a 25% reduction in system downtime and ensured high reliability- Developed and delivered in-depth training sessions for the BI team and produced comprehensive documentation, facilitating smooth system transition and long-term operational sustainability