Sr. Data Analyst Supply Chain

With a career at The Home Depot, you can be yourself and also be part of something bigger.

Key Responsibilities: • Leverage proven technical abilities to synthesize complex analytical tasks into easily understood data driven stories. • Responsible for working collaboratively among other talented analysts to apply established and create new analytical processes on diverse datasets to deduce insights and solve real-world business problems. • Ensure that all reporting and analytical responsibilities are completed competently in a timely manner, continually seek out opportunities to learn new and hone existing technical skills (e.g. writing SQL/code, statistics, machine learning, etc.) and operates under minimal supervision and mentorship of more experienced managers and data scientists. • Leverage data analytics tools to create new dashboards, reports, and any additional ad-hoc requests. • Execute existing reporting and analytical responsibilities. • Ensure the quality of work output by displaying a keen attention to detail. • Present findings in easily understood ways, focuses on how the data analytics fits into the bigger picture. • Develop additional technical competencies and subject matter expertise within core functional group. • Telecommuting available.

Minimum Qualifications: • Must be eighteen years of age or older. • Must be legally permitted to work in the United States. • Bachelor’s degree in Data Analytics, Business Analytics, Industrial Engineering, or a related field and 2 years of experience in a related occupation. Requires 2 years of experience in the following skills: • MS Excel, MS Access, SQL Server, Teradata, Oracle; • Writing complex SQL queries involving multiple joins, subqueries, Common Table Expressions (CTEs), and window functions; • Performance tuning and query optimization for large datasets; • Management of relational database schemas and writing stored procedures; • Building interactive dashboards and visualizations tailored to different business needs; • Using calculated fields, parameters, and level-of-detail (LOD) expressions; • Performance optimization of Tableau workbooks and managing dashboard publication to Tableau Server or Tableau Online; • Translating business questions into visual analytics solutions; • Using programming languages (such as Python) for data wrangling, cleaning, and transformation (using libraries including Pandas and NumPy); • Basic automation tasks, API integrations, and working with Jupyter Notebooks; • Data pipelines and lightweight ETL workflows.