Senior Data Analyst

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

This role focuses on building and maintaining a data warehouse platform, creating data visualizations and dashboards, and collaborating with data engineering teams to deliver insights. It involves SQL querying, data governance, and ensuring data quality and SLAs for data products.

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
  • Bachelor's degree in a STEM field or equivalent

Nice to have

  • Java
  • JavaEE
  • SpringBoot
  • JavaScript
  • JQuery
  • MySQL
  • AWS
  • Git & Github

What the JD emphasized

  • primary data partner
  • actionable insights
  • critical business questions
  • data warehouse
  • Data Engineering teams
  • Data Catalog
  • Data Dictionary
  • data governance
  • analytics platform's performance
  • service level agreements (SLAs)
  • data products
  • complex data analysis
  • mastery with SQL
  • dbt and Snowflake
  • relational and dimensional database structures
  • architect a data platform
  • Agile SCRUM teams
  • independent work skills