Data Analyst Graduate (Supply Chain and Logistics- Fulfillment Centers Operations) - 2027 Start

02 · Requirements and your timeline

Check the actual eligibility requirements.

Degree levels recorded

bachelors

Graduation window

No graduation window stated

Start date

Not confirmed

Deadline

No fixed deadline confirmed

Minimum years of experience

No numeric minimum confirmed

Applications are reviewed on a rolling basis; apply early. No fixed closing date stated.

Requirements summary: completing/recently completed bachelor’s in Business, Logistics, Operations, Engineering or related; SQL/Python and BI. Warehouse/e-commerce experience preferred.

• Sponsorship and CPT/OPT acceptance are not explicitly stated; ask recruiting before relying on eligibility.
• The role is based in Fontana; warehouse/fulfillment analysis differs from product analytics.
• Maximum two applications across TikTok and affiliates; reviewed in application order.
• 2027 is the advertised start cohort; precise graduation month bounds are not stated.

Questions still to resolve

• Confirm sponsorship for the specific role and your circumstances.
• E-Verify enrollment has not been matched to the hiring legal entity. Verify separately if relevant to STEM OPT.
• Compare your degree and enrollment status with the official requirements.
• No fixed deadline was confirmed; check the official posting before applying.

Graduation dates are compared only where exact bounds are stated. We do not infer CPT/OPT eligibility, work permission or H-1B timing from your academic year.

03 · Separate employer check

E-Verify

Hiring legal entity not independently confirmed

No official E-Verify legal-employer match was verified. This is not a finding that the employer is unenrolled.

E-Verify participation is separate from H-1B sponsorship and the other STEM OPT requirements. An employer statement or an unmatched name is not a completed legal-employer verification.

04 · Prepare with a purpose

What to prepare and why

Build an hourly fulfillment-performance dashboard in SQL/Python, analyze missing/damaged orders and capacity bottlenecks, and explain peak-season SLA trade-offs.