Accounting Analyst

Location: 400 Bertha Lamme Drive Cranberry Township PA 16066

Type: 4 months contract on W2

Schedule: hybrid3 day in the office. Must be able to come in on Tuesdays since that is when majority of the team works inoffice.

Summary:

As an Analyst for our clients Architectural Coatings Stores you will support the retail store operations in the United States and Canada (USCA). This business is large and sophisticated which will require you to work with a substantial volume of transactions in an efficient detailed manner. If you excel at multitasking driving efficiency within a team and can build trust among your colleagues as well as the many departments the team collaborates with; this role is for you!

Key Responsibilities:

• Compile weekly AR Adjustment data ensuring accuracy and adequate approval and post to the general ledger (goodwill tax adj etc.)
• Research discrepancies between goods received not invoices and intransit liability discrepancies. Import and export data to a secondary reconciliation tool (TRECS) monthly.
• Monitor the price override process and perform sample audit procedures monthly. (These are the differences between Xstore GR and SAP IR) by PO and clear balance to the PPV acct)
• Process prepaid deposits requested by the retail store locations
• Research and correct GRNI
• Record monthly journal entries including accruals reclassifications and account analysis corrections while processing prepaid deposits requested by retail store locations.
• Prepare account analysis of key and highrisk general ledger accounts.
• Correspond with individuals outside of the department as needed.
• Assist with operational improvements to streamline and simplify processes.
• Help with other team tasks as needed

Qualifications:

• Bachelors degree in Accounting Finance or equivalent education with 2 year of proven analytical finance accounting or business experience
• Experience using Excel is required.
• Experience using SAP is desired.
• Very strong analytical and organizational skills
• Familiarity working with large data sets
• Ability to problem solve independently