Hybrid Sales Operations Analyst – Data-Driven Revenue

Intel Corporation; Austin (TX); Hybrid; USD 81,000 - 165,000; Full time

Job summary

Intel Corporation is seeking an experienced Sales Operations Analyst to bridge sales, business units, customers, and operations teams. The role drives results across supply chain, forecasting, and revenue optimization by transforming complex data into actionable insights and by improving business processes.

The position emphasizes collaboration across sales, factory, and customer interfaces, with a focus on data quality, governance, and efficient order-to-cash processes.

Qualifications

• Bachelor's degree in business, supply chain, engineering, finance, or a related field.
• 3+ years of experience in the semiconductor or manufacturing industry.
• 3+ years of experience with SAP, including SAP Sales and Distribution (Order Management).

Responsibilities

• Serve as a communication conduit between customers, sales teams, business units, and Intel factories to optimize supply and revenue.
• Manage the sales process and back-office, act as the conduit between Customer, Sales, and Factory to optimize revenue and supply.
• Partner cross-functionally within and across sales teams to ensure data quality, availability, and governance.
• Develop, implement, and enhance business processes for customer supply chain management and compliance measurements.
• Manage order types/invoice points, expedite requests, change requests and RMAs.

Skills

SAP SD

Data analysis

Cross-functional collaboration

Education

Bachelor's degree in business or related field

Tools

SAP S/4HANA

Job description

Intel Corporation is seeking an experienced Sales Operations Analyst to bridge sales, business units, customers, and operations teams. The role drives results across supply chain, forecasting, and revenue optimization by transforming complex data into actionable insights and by improving business processes.

The position emphasizes collaboration across sales, factory, and customer interfaces, with a focus on data quality, governance, and efficient order-to-cash processes.