Senior Data Analyst

Metropolis Metropolis · Vertical AI · Chicago, IL +8 · Data Engineering & Analytics

This role focuses on building and maintaining a data warehouse platform, including data catalog and governance, and providing analytics insights through dashboards and SQL queries. It involves collaboration with data engineering and business teams, and mentoring junior members.

What you'd actually do

  1. Serve as the primary data partner for key business teams, helping them define their analytics roadmaps and prioritize projects that drive business value
  2. Develop data visualizations, dashboards, and reports using Tableau to provide teams with actionable insights
  3. Craft complex SQL queries and perform ad hoc analysis to answer critical business questions and evaluate the impact of new features
  4. Translate complex data findings into clear, compelling stories for both technical and non-technical audiences
  5. Contribute to the evolution of the data warehouse by collaborating with Data Engineering teams

Skills

Required

  • 6+ years of experience in an Analytics or Business Intelligence role
  • 4+ years of experience performing complex data analysis and demonstrating mastery with SQL
  • 4+ years of experience developing visualizations and dashboards in Tableau or a similar BI tool
  • 2+ years of experience using dbt and Snowflake
  • Expertise in relational and dimensional database structures, principles, and best practices
  • Proven ability to collaborate with Data Engineering teams to architect a data platform
  • Experience working in Agile SCRUM teams
  • Strong project management and independent work skills

Nice to have

  • Bachelor's degree in a STEM field or equivalent

What the JD emphasized

  • primary data partner
  • data warehouse platform
  • Data Catalog or Data Dictionary
  • data governance
  • analytics platform's performance
  • service level agreements (SLAs) for data products
  • mastery with SQL
  • dbt and Snowflake
  • relational and dimensional database structures, principles, and best practices
  • collaborate with Data Engineering teams to architect a data platform