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.