Role Description
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
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Data Modeling & Visualization:
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Understand the basics for modeling and implement best practices for data visualization.
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Design performant data models using SQL and BI development tools.
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Functional/Technical Requirements:
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Collaborate as part of an Agile team with Product Managers, Analysts, Analytics Engineers, and Data Engineers.
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Translate technical and business concepts and apply data and BI solutions.
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Program/Portfolio Management Support:
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Work within an established program management plan to achieve specific goals.
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Support and maintain production processes and troubleshoot issues.
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Coordinate code review with engineering, data validation, and QA/UAT with analysts and business partners.
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Technical Developments Recommendation:
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Design, build, and deploy new data models and BI applications and enhance existing in production.
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Support efforts and suggest ways to optimize solutions to better meet business, performance, and/or quality needs.
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Ongoing Learning and Development:
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Develop own capabilities by participating in assessment and development planning activities as well as formal and informal training and coaching.
Qualifications
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Experience with Business Intelligence (BI) tools (e.g. Microsoft Power BI, QlikSense, Looker, Tableau).
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Experience with cloud platforms (e.g. Microsoft Azure, Google Cloud Platform (GCP)).
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Experience with cloud data warehouses (e.g. Snowflake, Google BigQuery).
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Experience with databases (e.g. Oracle).
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Experience with version control systems and CI/CD (e.g. GitHub, GitHub Actions).
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Development experience in SQL.
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Python development and data architecture experience preferred.
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Preferred experience with:
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Databricks:
Unity Catalog, SQL stored procedures, job orchestration, Databricks Asset Bundles (DABs), Metric Views, Genie.
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Power BI:
report/dashboard development, DAX measures, data modeling, Power Query.
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SQL:
advanced patterns including CTEs, MERGE/upsert, window functions, parameterized stored procedures, dimensional modeling.
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Python:
PySpark, notebook-based ETL workflows.
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Retail/merchandising analytics domain knowledge.
Education
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Bachelor's Degree or Equivalent Level Preferred.
Experience
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1-3 Years of Experience.
Virtual Requirements
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Cameras must be on during all virtual interviews.
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AI tools are not permitted to be used by the candidate during any part of the interview process.
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Offers are contingent upon a satisfactory background check which may include ID verification.
