Sharmistha Sahu

• Designed, developed, and maintained robust data pipelines to facilitate the seamless flow of information within the organization, ensured data quality and reliability through comprehensive data validation and error-handling mechanisms
• Managed Azure Data Lake Storage to optimize data processing time and facilitate the daily processing of substantial data volumes and implemented robust data access controls and encryption measures, ensuring a high data availability rate and compliance with data security standards

Designed, developed, and maintained robust data pipelines to facilitate the seamless flow of information within the organization
Ensured data quality and reliability through comprehensive data validation and error-handling mechanisms
Managed Azure Data Lake Storage to optimize data processing time and facilitate the daily processing of substantial data volumes
Implemented robust data access controls and encryption measures
Ensured a high data availability rate and compliance with data security standards
Orchestrated the development of sophisticated Extract, Transform, Load (ETL) processes
Demonstrated a keen understanding of data engineering principles
Through meticulous design and implementation, achieved a substantial improvement in data integration efficiency
Utilized Azure Databricks to develop and deploy advanced data analytics solutions
Leveraged Apache Spark for data processing
Resulted in reduction of processing time for large datasets
Adopted scalable data pipelines, enabling the efficient analysis of petabytes of data for actionable insights
Initiated Azure Data Factory to automate data integration and transformation
Reduced data processing time
Implemented robust data monitoring and alerting for enhanced reliability
Collaborated cross-functionally to deliver timely, error-free data solutions
Improving operational efficiency and enabling data-driven decisions
Optimized Azure Blob Storage for scalable and secure data storage solutions
Implemented data lifecycle management policies
Reducing storage costs
Ensured data availability
Established data quality standards and implemented data governance processes
Improvement in data accuracy with teams to streamline data storage and retrieval processes
Enhancing overall data management efficiency
Evaluated complex mapping and transformation processes through data migration
Data accuracy rate post-migration closely with stakeholders to define migration objectives
Minimize downtime
Meet project deadlines
Facilitating a smooth transition and preserving data integrity
Improved data accessibility by implementing data warehouse optimizations
Reduction in report generation time
Collaborated with data scientists and analysts to understand business requirements and optimize data processes

Designed scalable and cost-effective data architectures on Microsoft Azure
Leveraging services such as Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake Storage
Orchestrated data pipelines in Azure Data Factory
Achieving improvement in ETL efficiency and reducing data processing costs
Monitored solutions to result in reduction in incident response time and ensuring pipeline reliability
Led the design and implementation of data warehousing solutions on Azure Synapse Analytics
Improvement in query performance and reduction in data loading times
Achieved optimal resource utilization, contributing to a reduction in overall data processing costs
Managed data storage and retrieval in Azure Data Lake Storage
Enhancing data access speed and significantly improving overall query performance
Implemented effective data partitioning strategies
Contributing to a reduction in storage costs
Initiated the deployment and optimization of Azure Synapse Analytics (formerly Azure SQL Data Warehouse)
Improving query response times and reducing data warehouse processing costs by implementing data compression strategies
Leading to a reduction in storage consumption and contributing to enhanced overall system efficiency
Developed and optimized data storage in Azure Blob Storage
Implementing lifecycle management policies and efficient data practices
Achieved high availability rates
Ensuring reliable data accessibility for critical applications
Streamlined ETL processes
Significantly reducing data processing time and improving overall data transformation efficiency and quality checks
This led to a notable decrease in post-release defects
Ensuring high-quality data integration
Implemented big data solutions on Azure, utilizing services such as Azure Databricks and HDInsight to enhance data processing speed and reduce resource utilization
This optimization contributes to cost efficiency and overall improvements in big data analytics capabilities
Executed complex data migration projects
Ensuring seamless transition of data between systems
Meticulous data validation
Minimizing downtime
Resulting in successful data migrations with minimal disruption to business operations

Implemented Agile methodologies
Enhancing project delivery efficiency with increased sprint velocity and a notable reduction in time-to-market for software releases
Demonstrated proficiency in requirements analysis
Leading to a reduction in project scope changes and improved accuracy in initial project estimations
This contribution positively impacted project planning and successful execution
Developed the system design phase
Optimizing architecture to achieve improved application scalability
This was evident in increased user load capacity and a reduction in system response times
Demonstrated coding expertise
Consistently delivering high-quality code through successful code reviews
Contributing to a reduction in post-release defects
Showcase a commitment to reliable software development
Drove comprehensive testing initiatives to increase test coverage and decrease post-release defects
Ensured the delivery of robust and high-quality software solutions
Led efficient deployment processes to maintain a high system uptime and reduce deployment-related incidents
Ensuring seamless operation and minimizing disruptions for end-users
Coordinated cross-functional collaboration efforts
Significantly boosting team productivity
Received outstanding ratings in team satisfaction surveys
Highlighting successful teamwork and fostering positive working relationships