Analytics Engineer II

At DICK’S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve. Overview Design and build data models and BI dashboards, data visualizations, and applications to solve business problems. Support Analytics Engineering technical solutions and best practices using requirements and direction. Responsibilities Understand the basics for modeling and implement best practices for data visualization. Design performant data models using SQL and BI development tools. Collaborate and work as part of an Agile team with Product Managers, Analysts, Analytics Engineers, and Data Engineers to understand data and business needs. Translate technical and business concepts and apply data and BI solutions. Understand how to work within an established program management plan to achieve specific goals. Support and maintain production processes and effectively troubleshoot issues. Coordinate code review with engineering, data validation, and QA/UAT with analysts and business partners. Design, build, and deploy new data models and BI applications and enhance existing in production. Support efforts and suggest ways to optimize solutions to better meet business, performance, and/or quality needs. Develop own capabilities by participating in assessment and development planning activities as well as formal and informal training and coaching. Qualifications Technology Experience Experience with Business Intelligence (BI) tools (e.g. Microsoft Power BI, QlikSense, Looker, Tableau); cloud platforms (e.g. Microsoft Azure, Google Cloud Platform (GCP)); cloud data warehouses (e.g. Snowflake, Google BigQuery); databases (e.g. Oracle); version control systems and CI/CD (e.g. GitHub, GitHub Actions). Development experience in SQL. Python development and data architecture experience preferred. Preferred Experience Databricks – Unity Catalog, SQL stored procedures, job orchestration, Databricks Asset Bundles (DABs), Metric Views, Genie Power BI – report/dashboard development, DAX measures, data modeling, Power Query SQL – advanced patterns including CTEs, MERGE/upsert, window functions, parameterized stored procedures, dimensional modeling Python – PySpark, notebook-based ETL workflows Retail/merchandising analytics domain knowledge Education Bachelor's Degree or Equivalent Level Preferred Experience 1-3 Years of Experience #J-18808-Ljbffr