Ensure the availability, performance, and scalability of data infrastructure. Collaborate with data engineering and analytics teams to design, implement, and maintain efficient Extract, Transform, Load (ETL) processes. Utilize Azure Data Factory and other data integration tools to orchestrate and automate data workflows. Implement and enforce data security measures to protect sensitive information stored in Azure data services. Ensure compliance with data governance policies, industry regulations, and privacy standards. Work closely with data architects to design and implement data models that support business requirements. Optimize data structures and schemas for performance and scalability. Provide expert-level support for data-related incidents, diagnosing and resolving issues promptly to minimize data disruptions. Collaborate with cross-functional teams to investigate and resolve complex data-related technical problems. Develop and maintain automation scripts using PowerShell, Python, or other relevant technologies to automate data-related tasks.

Wrote and submitted comprehensive bug reports, providing details on factors such as ease of replication, severity, and other metrics. They provided subjective feedback to shape game development by applying personal and third-party taste profiles. Completed in-depth usability testing on Windows, Android, and iOS mobile devices. Gathered data on integration issues and vulnerabilities, reported findings, and recommended improvements. Used PowerShell to automate simple tasks like starting/stopping Redshift clusters, scheduling backups, or restarting services. Develop and maintain data pipelines using tools like AWS Data Pipeline, Spark on Redshift, or custom Python/Bash scripts. Extract, transform, and load data into Redshift from various sources (databases, APIs, log files). Used PowerShell cmdlets to retrieve basic Redshift cluster metrics like cluster status, number of active queries, disk space usage, etc

Develop and maintain data pipelines and data warehouses using tools like MongoDB, BigQuery, and Google Cloud Dataproc. Write and execute SQL queries to extract and transform data using tools like DataGrip. Build and maintain machine learning models using tools like Google Cloud AutoML and TensorFlow. Work with data scientists and engineers to develop and implement data-driven solutions. Create and maintain data visualizations and dashboards using tools like Google Data Studio and Tableau. Troubleshoot and debug data-related issues, including performance and accuracy problems. Stay up-to-date on the latest data technologies and trends by attending conferences, reading blogs, and taking online courses. used Sap Hybris streamline data extraction for further analysis, ultimately improving the product information and shopping experience on the AEM-managed website. used AEM, a content management system, to manage the user experience of the website. By integrating with SAP Hybris, you've ensured that product information is displayed accurately and consistently, creating a seamless shopping experience for customers.