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Optomi

Data Analyst Job at Optomi in Detroit

Optomi, Detroit, MI, United States, 48228


Job Description:


Optomi, in partnership with a leading financial company, is seeking a Data Analyst to join their team!


**This position is hybrid 3X a week on-site in Detroit, MI**


Qualifications:

  • 5+ years of professional IT experience.
  • Proven experience as a Data Analyst or in a similar role.
  • Strong proficiency in SQL for querying databases.
  • Experience with data analysis and visualization tools (e.g., Excel, Power BI, Tableau, Google Data Studio).
  • Knowledge of statistical analysis techniques and software (e.g., R, Python, SAS).
  • Ability to analyze large datasets and derive meaningful insights.
  • Solid understanding of data management principles and best practices.
  • Strong attention to detail, with the ability to identify discrepancies in data.
  • Excellent communication skills to present findings to both technical and non-technical stakeholders.
  • Strong problem-solving skills and critical thinking ability.


Key Responsibilities:

  • Migrate remaining reports that are Access based SQL to SAS SQL working out of Snowflake platform.
  • Collect and organize data from various internal and external sources.
  • Clean and preprocess data to ensure accuracy, consistency, and completeness.
  • Perform statistical analyses to identify trends, correlations, and patterns in data.
  • Create reports, dashboards, and visualizations that effectively communicate insights to stakeholders.
  • Work closely with cross-functional teams (marketing, finance, operations, etc.) to understand data needs and provide actionable insights.
  • Develop and maintain data models to support business requirements.
  • Conduct ad hoc analysis and provide data-driven recommendations on business strategies and performance.
  • Ensure the integrity and reliability of data through validation and quality checks.
  • Present findings and insights in a clear and understandable manner, including using visual storytelling techniques to make complex data accessible.