Senior Data Analyst

• Analyze existing datasets, pipelines, data models, interfaces, and business rules to understand current-state solutions and dependencies.
• Design, build, refactor, and optimize data models, pipelines, transformations, and analytical datasets.
• Use SQL and data analysis to investigate complex business and operational problems and translate findings into actionable solutions.
• Support modernization and portfolio rationalization by identifying opportunities to consolidate, standardize, migrate, or decommission legacy data solutions.
• Partner with Product Managers, architects, engineers, analysts, and business stakeholders to translate requirements into scalable technical solutions.
• Apply strong data quality, validation, testing, lineage, and documentation practices.
• Develop datasets and data solutions supporting BI and visualization platforms such as Power BI and Tableau

Qualifications

• 5+ years of experience in Data Analytics, Data Engineering, BI, Data Warehousing, or related disciplines.
• Strong SQL skills and experience analyzing large, complex datasets.
• Hands-on experience with data modeling, data pipelines, transformations, and data architecture.
• Experience with modern data/cloud platforms, particularly Databricks, as well as Azure, AWS, or Google Cloud.
• Experience supporting BI and semantic-layer technologies such as Power BI, Tableau, Cognos, SSAS, or Azure Analysis Services.
• Experience using AI-enabled data and development tools, including Databricks Genie and Cursor, to accelerate data analysis, development, and solution delivery.
• Strong analytical and problem-solving skills with the ability to translate business processes and requirements into data solutions.
• Experience working in Agile/Scrum or Kanban delivery environments.
• Ability to work independently, manage deliverables, and communicate effectively with both technical and business stakeholders.
• Supply chain, order fulfillment, order management, or related enterprise data experience is preferred.
• Able to quickly understand an existing enterprise data landscape and become productive with limited oversight.
• Comfortable working hands-on across analysis, solution design, engineering, testing, documentation, and implementation.
  • Expected to deliver maintainable solutions aligned with established architecture and data standards.