• Demonstrated proficiency in Azure Blob and Data Lake storage systems, adeptly managing data loading processes into Azure SQL Synapse Analytics.• Designed and implemented linked services to facilitate seamless data transfer from SFTP locations and Blob storage to Azure Data Lake, ensuring efficient data ingestion and storage in a cloud-based environment.• Utilized Azure Data Factory (V2) to create robust pipeline jobs, implement scheduling triggers, and design data flow mappings, demonstrating extensive expertise in orchestrating complex data workflows. • Developed and implemented Azure Cloud relational servers and databases, meticulously analyzing present and future business needs to ensure alignment with organizational objectives.• Implemented complex data transformations and aggregations using PySpark DataFrame API and SQL functionalities, facilitating insightful data analysis and modeling.• Integrated Airflow with various data sources and systems such as databases, cloud storage, and APIs, enabling seamless data ingestion, transformation, and loading processes.• Integrated Azure Monitor with Azure Resource Manager templates and Azure Policy to enforce compliance with organizational standards and best practices for resource provisioning, management, and security within project environments.• Established synchronization of user identities and authentication across hybrid environments by configuring Azure Active Directory to seamlessly connect with on-premises Active Directory environments through Azure AD Connect.

• Monitored and maintained ETL jobs, performance metrics, and data pipelines to ensure data availability, reliability, and scalability, proactively addressing issues and optimizing processes as needed.• Designed and implemented data storage architectures using Azure Blob Storage to accommodate diverse data types and workloads, ensuring scalability, durability, and high availability of stored data.• Designed and developed scalable data pipelines in Azure Databricks, orchestrating complex ETL processes to ingest, transform, and load data from disparate sources.• Spearheaded the utilization of Apache Spark within project environments, leveraging its distributed computing capabilities to process and analyze large-scale datasets efficiently.• Configured Directed Acyclic Graphs (DAGs) parameters such as concurrency, execution timeout, and task retries to optimize resource utilization, performance, and fault tolerance in data processing workflows.• Leveraged specialized data formats such as Parquet and Avro within Spark environments for optimized storage and processing of large-scale datasets, significantly improving performance and scalability of data pipelines.• Implemented complex business logic and error handling mechanisms within Azure Logic Apps to ensure robust workflow execution and reliable handling of exceptions and edge cases.• Deployed Azure Log Analytics to aggregate and analyze log data from diverse sources, facilitating centralized monitoring and troubleshooting across Azure environments.

• Architected and implemented end-to-end analytics solutions using Azure Synapse Analytics, combining data warehousing, big data, and data integration capabilities to enable comprehensive data analysis and reporting.• Configured monitoring and alerting mechanisms within Azure Data Factory to track pipeline execution, monitor data quality, and detect and respond to errors or performance issues in real-time, ensuring data pipeline reliability and integrity.• Designed and configured storage accounts and containers within Azure Blob Storage to organize and categorize data according to project requirements and access patterns.• Implemented security measures such as Azure Active Directory authentication, firewall rules, and data encryption within Azure SQL Database to protect sensitive data and ensure compliance with security standards.• Implemented OLTP (Online Transaction Processing) systems within project environments to support real-time transactional workflows and data processing, ensuring efficient handling of high-volume transactions.• Conducted performance tuning and optimization of PostgreSQL databases, fine-tuning configurations, and indexing strategies to improve query execution times and resource utilization.• Implemented Data modeling best practices such as data normalization, indexing, and partitioning to ensure data integrity, minimize redundancy, and optimize database performance.• Implemented monitoring solutions using Azure Monitor Metrics and Azure Monitor Logs to collect, analyze, and visualize telemetry data from diverse sources such as virtual machines, containers, and applications, facilitating real-time monitoring and analysis.

• Developed ETL (Extract, Transform, Load) pipelines to ingest data from various sources into Amazon Redshift, leveraging tools such as AWS Glue, Apache Spark for data integration and transformation.• Implemented Amazon S3 within project environments as a scalable and durable object storage solution, enabling cost-effective storage and retrieval of data at any scale.• Implemented security controls such as fine-grained access control policies and encryption-at-rest within DynamoDB to protect sensitive data and ensure compliance with security standards.• Designed and configured data pipelines within Amazon Data Pipeline to extract data from diverse sources, transform it using EMR clusters and load it into target destination Amazon S3.• Monitored and analyzed database performance metrics and logs in Amazon RDS using Amazon CloudWatch and RDS Performance Insights, identifying performance bottlenecks and optimizing database performance.• Conducted data migration and integration tasks between different database systems or versions using SQL scripts and ETL (Extract, Transform, Load) tools.• Utilized AWS Lambda to develop and deploy serverless functions in Python, effectively executing business logic, data processing, and integration operations within the project.