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Data Analyst Executive

MR DIY International

Sep 09
data-analyst Mid (2-5 yrs) Full-time
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About the role

The Data Analyst will collect, clean, and validate data to generate actionable insights and trends for the retail business. They are responsible for developing dashboards and reports while collaborating with stakeholders to define key performance indicators.

What they look for

Data Analytics Data Modeling Statistics Data Cleaning Data Validation Dashboard Development Reporting Data Visualization Excel KPI Definition Data Governance Quality Assurance Automation Data Pipelines

Requirements

The role requires proficiency in data modeling, statistics, and data visualization tools like Excel. Candidates must be detail-oriented with the ability to translate complex analytical findings into clear business recommendations.

Full description

Job Summary:

We are seeking a detail-oriented and reliable Data Analyst to join our team. The Data Analyst will be responsible for data analytics, data modeling, and statistics to support business decisions in the retail industry. The role involves analyzing large data sets, generating insights and identifying trends, as well as communicating findings to stakeholders and presenting actionable recommendations that support organizational goals. The Data Analyst will also ensure data accuracy and integrity, and develop reporting tools and dashboards to enhance data-driven decision-making.

Key Responsibilities:

  • Collect, clean, and validate data from multiple sources to ensure accuracy and consistency.
  • Analyze complex datasets to identify trends, patterns, and insights.
  • Develop and maintain dashboards, reports, and visualizations using tools such as Excel.
  • Work closely with stakeholders to define metrics, KPIs, and data-driven business objectives.
  • Translate analytical findings into clear, actionable recommendations for business units.
  • Support data governance, quality assurance, and documentation efforts.
  • Automate repetitive data processing and reporting tasks where possible.
  • Collaborate with data engineers and IT teams to optimize data pipelines and storage solutions.

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