Key Responsibilities Manage Production/Development databases performance in the following areasReview Teradata system reports and provide “performance assessment” report with recommendations to optimize system.Investigate and quantify opportunities from “performance assessment” reports Monitor using Viewpoint tool for Teradata system performance using different portlets.Review poor performing queries generated from BI/ETL tools and provide best practice recommendations on how to simply and restructure views, apply PPI or other index changesStatistics Management and OptimizationOptimal index analysis - Review Index usage on tables and recommend adding dropping indexes for optimal data accessReview uncompressed tables, analyze its usage and implement compression to save space and reduce IO activity Optimize locking statements in views, macros & queries to eliminate blocking contention investReview the Spool Limits for the users Check for Mismatch data types in the system and make them unique to avoid costly translations during query processingReview Set tables and check for the options to convert to MultiSet to avoid costly duplicate row checking operationAnalyze various applications and understand the space requirements and segregate the disk space under the categories of perm, spool and temp spaceSetting up the database hierarchy that includes database creation and management of objects such as users, Roles, Profiles, tables, viewsMaintain profiles, roles, access rights and permissions for Teradata user groups and objects.Exposure to DWH Env (Knowledge of ETL/DI/BI Reporting).Generate periodic performance reports using PDCR and identify bottlenecks with the system performanceEffective usage of TASM & Priority distribution to penalize the resource intensive queries, Give high priority to business critical workloads, Throttling of different workloads for optimal throughput and provide performance reports to check workload management health.

- Curriculum Development: Designing and developing data science curriculum, course materials, and learning resources to ensure effective instruction and meet learning objectives.- Instruction Delivery: Delivering engaging and effective lectures, workshops, and training sessions on various data science topics, tools, and techniques to students or trainees.- Technical Guidance: Providing technical guidance and support to students/trainees, assisting them in understanding and applying data science concepts, algorithms, and methodologies.- Hands-on Exercises: Designing and overseeing hands-on exercises, assignments, and projects to reinforce learning and provide practical experience in data analysis, modeling, and visualization.- Assessments and Feedback: Creating assessments and evaluating the progress and performance of students/trainees, providing constructive feedback and guidance for improvement.- Mentoring and Guidance: Offering mentorship and guidance to individuals or teams, assisting them in solving data science problems, addressing challenges, and developing their skills.- Industry Trends and Research: Staying updated with the latest trends, techniques, and advancements in the field of data science, conducting research, and integrating industry best practices into the curriculum.- Collaboration: Collaborating with other instructors, subject matter experts, and stakeholders to ensure effective program delivery, alignment with learning objectives, and continuous improvement.- Support and Guidance: Providing support and guidance to students/trainees outside of instructional time, such as office hours or online forums, to address questions, clarify concepts, and offer additional assistance.- Course Evaluation and Improvement: Collecting feedback from students/trainees, analyzing course effectiveness, and making improvements to the curriculum, instructional methods, and materials as needed.

leading and mentoring aspiring data analysts, guiding them through their learning journey, and equipping them with the skills needed in this dynamic field.