Job Description
Join DataDrive Analytics' elite night shift team and transform raw data into actionable business intelligence! We're seeking a meticulous Data Analyst to support our global operations while the world sleeps. This remote-friendly hybrid role offers unparalleled flexibility and growth opportunities in Minneapolis' thriving tech corridor. Work with Fortune 500 clients, leverage cutting-edge analytics tools, and enjoy premium compensation with comprehensive benefits.
As a cornerstone of our overnight operations, you'll maintain real-time data pipelines and deliver critical insights before sunrise. Our collaborative night shift culture values work-life balance with premium overtime rates and exclusive team-building events. If you thrive in focused, independent environments and want to make an impact while others rest, this is your career-defining opportunity.
Responsibilities
- Analyze and interpret complex datasets to identify trends and anomalies during overnight shifts
- Develop automated reporting dashboards using SQL, Python, and Tableau for daily executive briefings
- Monitor real-time KPIs and alert stakeholders to critical operational deviations
- Collaborate with global teams to ensure data integrity across time zones
- Optimize ETL processes for overnight batch processing workflows
- Present insights through concise visualizations and written summaries
- Maintain documentation for data governance and compliance standards
Qualifications
- Bachelor's degree in Statistics, Computer Science, or related field (or equivalent experience)
- 3+ years of professional data analysis experience with night shift or remote work background
- Advanced proficiency in SQL and at least one scripting language (Python/R)
- Expertise in data visualization tools (Tableau/Power BI)
- Strong understanding of statistical methodologies and hypothesis testing
- Experience with cloud platforms (AWS/GCP/Azure) and big data technologies
- Exceptional problem-solving skills with attention to detail
- Ability to work independently with minimal supervision