Durga Prasanna Das

Experience

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  QuintilesIMS

  Apr 2012 - Jun 2017

  Princpl Engineer

  Responsibilities:• Works closely with Project stakeholders to develop work plan for Data Warehouse projects and keep the leadership aware of any issues• Create scalable data models and ETL data pipeline for multiple clinical trial projects.• Providing analytical reports to improve decision-making ability and risk management.• Providing accurate estimates for project development and implementation. Work with management to meet those estimates• Develop framework, metrics and reporting to ensure progress can be measured, evaluated and continually improved• Perform in-depth analysis of research information for the purpose of identifying opportunities, developing proposals and recommendations for use by management• Support the development of performance dashboards that encompass key metrics to be reviewed with senior leadership and sales management.• Extensive experience in DW redesign Projects, enhancing the warehouse performance and reducing ETL load time• Proficient in understanding Data warehouse pain points and understanding the underlying business requirements, as well as doing gap analysis and creating complex level of mappings. Understanding of complex data warehousing methodologies and architectures with Ralph Kimball methodologyTools and Technologies: Informatica 8.x/9.x, SQL, Oracle, Unix, Putty, PL/SQL, Shell Scripting, SpotfireAccomplishments:• Successful launch of below projects-• Trial Management Data hub• Subject Data hub• Master Project management• Special Achievement Award, 2014.• Shout out Award 2016.• Certification – Informatica Power Center Data Integration 9.x: Developer Specialist. Show less

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  Amazon Development Center

  Jun 2017 - now

  Data Engineer

  Responsibility: • Communicated with DS/RE and participated in requirement gathering, involved in data modeling, design reviews from the business user group• Setting up clusters, pipeline to ensure data needs of team are meet in along with making sure Operational needs are meet• Create reporting layer , generate aggregate tables create necessary data pull for automate WBR, MBR and QBR• Create Reports using QuickSight, Tableau and Excel based on the needs and availability of tools • Make use of different scripting language ,Data pipeline tools to create Data marts for the team• Pre-production data discovery , production setups for new models and maintaining post production activities for ML Models• Using Feature repository (FR) for generating different offline variables to be used by modeling toolsTools and Technologies: Datanet (ETLM), DJS, Hoot, Hubble, FR, Eider, Model Factory, Model Builder, CSC, RMP, Dryad, EDX, AWS kinesis, Redshift, Oracle, QuickSight, R, Shell Scripting, PythonAccomplishments:• Successfully launched Unified reporting solution using Amazon internal reporting tool (QuickSight) for India Machine Learning Team• Launched DNR (Delivered Not Received) prevention Model in 2018 using amazon internal tools such as (FR, Model Builder, Dryard)• Launched C-Rejects V3 (Customer Rejects) prevention Model in 2017 using amazon internal tools such as (Datanet, Model Builder) Show less