Data Specialist (With SAP backgraound)

Qualifications

• Bachelor’s degree in Data Analytics or Business.
• 2+ years in a similar analytical role (i.e. business analysis, sales data analysis).
• Superior oral and written communication skills.
• Must have experience with CRM/ICM tools and SAP.
• Must have experience with BI tools such as Qlik, Tableau, PowerBI.
• High level of organization and detail-oriented.
• Excellent time management.
• Demonstrated ability to multitask and prioritize.

The Data Quality Analyst’s role is to strategically manage various customer attributes ensuring high levels of data quality, integrity, availability, and privacy. The individual will also support the Regional Sales Analyst, providing various ad-hoc and reoccurring data analysis for a variety of projects and tasks. This includes monitoring key data quality metrics to ensure data accuracy, and standardizing/developing efficient approaches to daily reporting. The data quality analyst should focus on business benefits and strategic significance of the quality to the sales organization. Additionally, this individual will serve as a facilitator and liaison with other departments to address any customer/sales data discrepancies.

MAJOR DUTIES AND RESPONSIBILITIES

• Develop partnerships with regional sales analyst to understand their needs and provide expedited data review.
• Compile and communicate steps taken on customer cleansing analysis to ensure stakeholder tasks have been completed.
• Provide territory analysis, including sales rep coverage to understand assignment gaps.
• Evaluate majority sales owner to determine proper coverage and assignment of campaigns and opportunities.
• Assist in developing SOP’s to ensure consistent process across projects and activities.
• Provide CRM support for sales consultant knowledge checkouts to ensure sales team is properly leveraging the platform.
• Validate and ensure daily completeness of lab lens data while making sure to emphasize and communicate significant findings.
• Identify and manage the resolution of data quality issues, such as uniqueness, integrity, accuracy, and consistency in a timely fashion.