Job Description
Typical Day in the Role
The team manages client segmentation and prioritization to determine which clients are assigned to sellers.
They collaborate with finance to set seller targets and quotas.
The team owns tools for quota management and ensures accurate data uploads for compensation processes.
They focus on consolidating global seller data, maintaining data accuracy, and supporting compensation payouts.
Participate in Sales Operations data management and analysis needed for segmentation and quota every fiscal half, facilitating data processes across a large, global organization.
Extract data from multiple repositories to analyze trends and provide insights around sales motion performance and execution to help accelerate business performance.
Seek and react to feedback from stakeholders to drive improvements around scaled tooling and reporting capabilities enabling easy access to critical business insights and driving operational efficiencies.
Act as a technical thought partner and advocate for end user groups during the feature development cycle with our partner engineering teams.
Candidate Requirements
Working knowledge of best practices in data quality / data cleaning
Effective use of SharePoint or similar technology to facilitate collaboration with a systemic approach
Strong decision-making & critical thinking ability; able to develop unique methods of analysis where traditional methods do not yield results
Versatile communicator, able to leverage data and insights to inform and influence a wide range of partners and stakeholders confidently and effectively.
Technical skills and responsibilities for the role, including SQL, Power BI, Fabric, and Python. The candidate will manage client segmentation, seller quotas, and data accuracy, requiring attention to detail and troubleshooting skills.
Quick learning ability and adaptability to different systems and data sources were highlighted as important.
Attention to detail and quality control in handling data, particularly for quota and target calculations, were considered critical.
Preferred Qualifications :
Ability to make decisions in a fast-paced, rapidly changing environment.
Python knowledge is ideal but not mandatory
Demonstrated understanding of machine learning techniques (regression, classification, clustering, decision trees, etc.)
Experience in effective requirements gathering and synthesis, enabling rapid product development.
Experience with sales, quota, or compensation-related operations
Top Hard Skills Required + Years of Experience
Data Analyst • Redmond, WA