Job Description
Join our dynamic team at Mountain Peak Analytics, a leading data-driven consulting firm in the heart of Colorado Springs. We're seeking a detail-oriented Part-Time Data Analyst to transform complex datasets into actionable business insights. This flexible role offers the perfect opportunity for professionals seeking work-life balance while making a significant impact on our clients' growth strategies.
Our ideal candidate thrives in collaborative environments and possesses a passion for uncovering hidden patterns in data. You'll work closely with senior analysts to develop reports, visualize trends, and support data-driven decision-making across diverse industries including healthcare, retail, and technology.
This position requires 20-25 hours per week with flexible scheduling options, including remote work capabilities. If you're ready to leverage your analytical skills to drive meaningful business outcomes in a supportive, innovative setting, we encourage you to apply today.
Responsibilities
- Analyze complex datasets using SQL, Python, and Excel to identify trends and opportunities
- Create compelling data visualizations and dashboards using Tableau/Power BI
- Develop and maintain automated reporting systems for key business metrics
- Collaborate with cross-functional teams to translate data insights into actionable recommendations
- Ensure data accuracy through rigorous validation and quality control processes
- Present findings to stakeholders through clear, concise written and verbal communication
- Stay current with industry best practices and emerging data technologies
Qualifications
- Bachelor's degree in Data Science, Statistics, Mathematics, or related field
- 2+ years of professional data analysis experience
- Proficiency in SQL, Python (Pandas, NumPy), and Excel
- Experience with data visualization tools (Tableau or Power BI)
- Strong problem-solving skills with attention to detail
- Excellent written and verbal communication abilities
- Ability to manage multiple projects and meet deadlines in a fast-paced environment
- Portfolio demonstrating previous data analysis projects preferred