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Data Cleansing Analyst at Slalom (Atlanta, GA)

Join Atlanta’s growing team as a Data Analyst. The Data Analyst will provide master data support for a transformation project. This role will support the design and execution of transformational data, process, and technology to support modern information needs and design requirements. This role will assist in the identification of new data quality rules and cleansing activities to support the program.


 Key Responsibilities:



  • Provide analytical support to leads and efforts that require master data support and knowledge.

  • Collaborates with other team members to interpret business requirements and translate into master data cleansing needs

  • Partners with the data team to design of the future state data quality rules and data quality cleansing needs

  • Collaborates with master data, the business and IT leads on project execution

  • Provide SME knowledge of master data system table structures, interfaces, source system platforms, governance/maintenance processes, and technologies


 Key Skills/Qualifications:



  • 5-7 years of work experience in an analytical, supply chain, or operational field

  • Analytic and quantitative skills (including large amounts of data) and synthesis of conclusions

  • Apply knowledge and insights to solve master data business issues

  • Strong communication skills

  • Experience with reporting tools such as Excel, Microsoft Access, etc.

  • Team player


 Preferred skills:



  • Understanding of master data principles and fundamentals

  • SAP, Informatica, or other relevant information system experience

  • Data mining experience

  • Experience with data visualization tools

  • Green Belt Six Sigma Certification


Slalom Is An Equal Opportunity Employer And All Qualified Applicants Will Receive Consideration For Employment Without Regard To Race, Color, Religion, Sex, National Origin, Disability Status, Protected Veteran Status, Or Any Other Characteristic Protected By Law.


by via developer jobs - Stack Overflow
 

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