Temporary inventory data analyst needed

We are seeking a detail-oriented Data Analyst to support our team in collecting, cleaning, analyzing, and visualizing data to help drive business decisions. This is time-bound. The ideal candidate has strong analytical skills, and enjoys working with data. We need someone who can work carefully through prescribed analysis paths, produce clean summaries, validate their work, and help translate results into high-quality external-facing deliverables in collaboration with our marketing and creative teams. Project overview The project involves analyzing inventory behavior data to support a partner-facing report, presentation, or other external-facing product deliverable. The right person will help us examine patterns, summarize key metrics, and produce reliable outputs that can be reviewed internally and then adapted into polished materials for external partners. Key Responsibilities - Collect, clean, and validate data from multiple sources to ensure accuracy and consistency. - Analyze datasets to identify trends, patterns, and actionable insights. - Create dashboards and reports using Microsoft Excel, Power BI, or Tableau. - Write SQL queries to extract and manipulate data from databases. - Collaborate with cross-functional teams to understand business requirements and provide data-driven -recommendations. - Document data analysis processes and maintain data integrity. - Support ad hoc reporting and analysis requests. Required Skills - Good knowledge of Microsoft Excel, SQL, and Power BI or Tableau. - Experience with Python or R for data analysis is an advantage. - Analytical, problem-solving, and communication skills. - High attention to detail and ability to work with large datasets. - Careful attention to joins, denominators, duplicate records, missingness, and time-window definitions - Ability to create clean tables and simple charts - Ability to explain findings clearly without overstating conclusions Preferred Skills - Experience with data visualization tools. - Understanding of statistics - Knowledge of data cleaning and preprocessing techniques. - Ability to work as part of a team. Deliverables may include: - Reproducible SQL, Python, or R scripts/notebooks - Clean summary tables - Simple charts suitable for internal review or partner-facing adaptation - Short written summaries of findings - Plain-English takeaways that can support partner-facing materials - Documentation of data filters, definitions, assumptions, and known limitations - QA checks showing that key counts and totals were validated - Review support for marketing or creative materials to ensure analytic claims, charts, and captions are accurate We will divulge more information as we proceed.