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Information Technology 🏢 Part Time ⭐️ Verified

Data Analyst (Weekend Shift)

DataDriven Solutions
Portland
Estimated Salary
USD 52.000 – USD 62.400
New
Live Update
6 Mei 2026
Deadline
6 Mei 2027

Job Description

Join DataDriven Solutions as a Weekend Data Analyst and transform raw data into actionable insights for our clients. This part-time role offers the perfect blend of analytical challenge and flexible scheduling, ideal for professionals seeking weekend-focused opportunities. You'll work in our vibrant Portland office, collaborating with cross-functional teams to drive data-driven decision-making.

We're looking for a detail-oriented analyst who thrives in fast-paced environments and is passionate about uncovering trends. If you're ready to leverage your skills to solve complex problems while maintaining a balanced work-life schedule, this role is for you.

Responsibilities

  • Analyze large datasets using SQL, Python, and Excel to identify trends and anomalies
  • Create compelling data visualizations and dashboards using Tableau/Power BI
  • Generate weekly performance reports for stakeholders across departments
  • Collaborate with business teams to translate data requirements into technical solutions
  • Maintain data integrity through rigorous validation and quality checks
  • Support ad-hoc analysis requests during weekend shifts
  • Document processes and maintain data dictionaries for ongoing reference

Qualifications

  • Bachelor's degree in Data Science, Statistics, Computer Science, or related field
  • 2+ years of experience in data analysis or similar role
  • Proficiency in SQL, Python (Pandas, NumPy), and Excel
  • Experience with data visualization tools (Tableau/Power BI)
  • Strong problem-solving skills with attention to detail
  • Ability to work independently during weekend shifts (Sat-Sun)
  • Excellent communication skills for presenting complex findings
  • Portfolio demonstrating past analytical projects

Required Skills

SQL Python Tableau Power BI Excel Data Visualization Statistical Analysis Data Cleaning

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