Sr Data Analyst

The Senior Data Analyst plays a crucial role in extracting actionable insights from large sets of data to inform strategic business decisions. They are responsible for analyzing complex datasets, creating visualizations, and presenting findings to key stakeholders. The role requires a combination of technical expertise, analytical skills, and effective communication to contribute to the overall success of the organization.

Responsibilities

• Utilize automated tools to extract data from primary and secondary sources.
• Removal of corrupted data and fixing coding errors and related problems
• Develop and maintain databases, and data systems – reorganizing data in a readable format.
• Perform analysis to assess the quality and meaning of data.
• Filter Data by reviewing reports and performance indicators to identify and correct code problems.
• Utilize statistical tools to identify, analyze, and interpret patterns and trends in complex data sets could be helpful for the diagnosis and prediction.
• Assign numerical value to essential business functions so that business performance can be assessed and compared over periods of time.
• Analyze local, national, and global trends that impact both the organization and the industry.
• Prepare reports for the management stating trends, patterns, and predictions using relevant data.
• Collaborate with other members and management heads to identify process improvement opportunities, propose system modifications, and devise data governance strategies.
• Prepare final analysis reports for the stakeholders to understand the data-analysis steps, enabling them to take important decisions based on various facts and trends.
• Performs other related duties as assigned.

Qualifications

• Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Computer Science, or related discipline.
• Salesforce Experience.
• Proven experience as a Data Analyst, with at least 3 years in a senior or lead role.
• Proficiency in data analysis tools such as Python, R, or SAS.
• Strong SQL skills for data extraction and manipulation.
• Experience with data visualization tools (Tableau, Power BI, etc.).
• Solid understanding of statistical concepts and modeling techniques.
• Knowledge of machine learning algorithms and techniques.
• Experience with big data technologies (Hadoop, Spark, etc.).
• Industry-specific knowledge (e.g., finance, healthcare, e-commerce).
• Excellent communication skills, with the ability to convey complex findings to both technical and non-technical audiences.