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Business Analyst - Aml

Infosys
June 13, 2024 12 0-1 Preferred by Company
B.Com / B.Tech/ B.E./ M.Com/ B.Sc / MBA / M.Tech/ M.E./ M.Sc/ BBA/ BA/ Any Graduate Pune
Full Time Python Communication Skills Power Bi Numpy Pandas Sql Data Analysis
Job description

About the job

Capture needs of business owners and create BRD, Functional, and Technical specifications and validate the final developed product/application to make sure all business needs and technical requirements are fulfilled.
 

  • Performs Data Analysis to gather and identify the underlying data required to fulfill business requirements/application scope.

  • Develop epics, storyboards, and user stories with stakeholders

  • Collaborate with development team for new product/application development

  • Creates functional and non functional test plans and writes test cases covering various scenarios.

  • Coordinate between multiple scrum teams working on the common product backlog within the department

  • Drive transformation of connectivity between legacy systems and design new cleaner pipes to reduce overhead Domain : Anti Money Laundering Roles:

  • As a Business Analyst, act as a liaison between business, QA and technology team.

  • Capture needs of business owners and create BRD, Functional, and Technical specifications and validate the final developed product/application to make sure all business needs and technical requirements are fulfilled.

  • Performs Data Analysis to gather and identify the underlying data required to fulfill business requirements/application scope.

  • Develop epics, storyboards, and user stories with stakeholders

  • Collaborate with development team for new product/application development

  • Creates functional and non functional test plans and writes test cases covering various scenarios.

  • Coordinate between multiple scrum teams working on the common product backlog within the department

  • Drive transformation of connectivity between legacy systems and design new cleaner pipes to reduce overhead Design a practical and effective data rationalization methodology to reduce redundancy of variables across different sources

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