وظيفة أخصائي تحليل بيانات شاغرة لدى نينجا بالرياض

We are looking for a Data Analyst to transform raw operational and transactional data into actionable insights that support business decisions and improve operational performance.

The ideal candidate has strong SQL skills, advanced Google Sheets expertise, and the ability to translate business requirements into meaningful analysis, reports, and dashboards. You will work closely with cross-functional teams to identify trends, investigate performance issues, and support data-driven decision-making.

Key Responsibilities

• Write advanced SQL queries using CTEs, window functions, and multi-table joins to extract, aggregate, and analyze large datasets.
• Build and maintain dynamic dashboards, forecasting models, and operational trackers using Google Sheets.
• Conduct root-cause analysis, identify trends, and investigate operational performance issues.
• Define and monitor KPIs to measure business and operational performance.
• Translate business requests into structured data models and actionable insights.
• Present analytical findings and recommendations to cross-functional stakeholders and leadership.
• Develop automated audit checks to ensure data accuracy, consistency, and quality.
• Support continuous improvement through data-driven recommendations.

Requirements

• 2-3 years of experience in Data Analytics or Business Intelligence.
• At least 2 years of hands-on experience writing advanced SQL queries.
• Strong SQL knowledge, including CTEs, window functions, and complex joins.
• Advanced Google Sheets skills, including QUERY, IMPORTRANGE, ARRAYFORMULA, XLOOKUP/VLOOKUP, and dynamic Pivot Tables.
• Experience with data visualization tools such as Looker Studio or Power BI.
• Strong analytical thinking, problem-solving, and root-cause analysis skills.
• Experience working with databases such as BigQuery, PostgreSQL, MySQL, or Snowflake.
• Excellent English communication skills; professional Arabic is strongly preferred.
• Bachelor’s degree in a relevant field is preferred.