aKube, Inc.
Job Description
City: Kissimmee FL/ Remote
Onsite/ Hybrid/ Remote: Remote
Duration: 12 weeks
Rate Range: Up to$64/hr on W2 depending on experience (no C2C or 1099 or sub-contract)
Work Authorization: GC, USC, All valid EADs except OPT, CPT, H1B
Must Have:
City: Kissimmee FL/ Remote
Onsite/ Hybrid/ Remote: Remote
Duration: 12 weeks
Rate Range: Up to$64/hr on W2 depending on experience (no C2C or 1099 or sub-contract)
Work Authorization: GC, USC, All valid EADs except OPT, CPT, H1B
Must Have:
- 3-5+ years of Data Analysis experience
- Strong proficiency in SQL and Excel (pivot tables, v-lookups, data modeling)
- Hands-on experience with at least one data visualization tool (Tableau, Power BI, or similar)
- Data aggregation and visualization project experience
- Prior experience as a Data Analyst for a retailer
- Understanding of product data
- Conduct detailed data analysis and ensure reporting accuracy across multiple jurisdictions.
- Develop and maintain dashboards, reports, and visualizations using BI tools (Tableau, Power BI, etc.).
- Acquire, filter, and clean large datasets from multiple sources to ensure data quality.
- Identify trends, patterns, and inconsistencies in datasets to drive insights.
- Partner with business stakeholders to gather requirements and deliver relevant reporting solutions.
- Support business intelligence capability development to improve reporting and decision-making.
- Communicate with international suppliers and internal teams to facilitate data collection and ensure compliance.
- Maintain thorough documentation of data projects, processes, and methodologies.
- Bachelor's degree in Data Analytics, Accounting, Finance, or related field.
- 3-5+ years of proven experience in data analysis, ideally in retail.
- Proficiency with SQL and Excel, including advanced functions and data modeling.
- Experience building visualizations and dashboards with BI tools such as Tableau or Power BI.
- Strong analytical mindset with attention to detail and accuracy.
- Ability to manage and interpret large datasets effectively.
- Strong communication, organizational, and problem-solving skills.
- Self-motivated, organized, and solution-oriented.
- Familiarity with Python or other data processing tools.
- Experience with packaging materials and retail product categorization.