Your main responsibility would be to gather, and interpret large sets of data to help make data-driven decisions.
Requirements
Data Analysis Tools: Proficiency in using data analysis tools such as SQL, Python, R, or similar programming languages for data manipulation, cleaning, and analysis.
Statistical Analysis: Strong knowledge of statistical concepts and methods, including hypothesis testing, regression analysis, and data modeling.
Data Visualization: Experience in creating visualizations and dashboards using tools such as Tableau, Power BI, or similar platforms to present data insights effectively.
Data Interpretation: Ability to interpret complex data and translate it into actionable insights and recommendations for business stakeholders.
Data Management: Familiarity with data management principles and best practices, including data cleansing, data integration, and data governance.
Data Warehousing: Understanding of data warehousing concepts and experience in working with data warehouse systems or platforms.
Problem-Solving Skills: Strong analytical and problem-solving skills to identify patterns, trends, and anomalies in data and propose solutions or optimizations.
Data Mining: Proficiency in data mining techniques and tools to discover hidden patterns or relationships within large datasets.
Data Quality Assurance: Knowledge of data quality assurance processes to ensure data accuracy, consistency, and integrity.
Reporting and Documentation: Ability to prepare comprehensive reports and documentation to communicate data analysis findings, insights, and recommendations.
Communication Skills: Excellent verbal and written communication skills to effectively communicate technical concepts and findings to both technical and non-technical stakeholders.
Attention to Detail: Strong attention to detail to ensure accuracy in data analysis and reporting.
Business Acumen: Understanding of business processes, key performance indicators (KPIs), and the ability to align data analysis with business objectives.
Teamwork and Collaboration: Ability to collaborate effectively with cross-functional teams, including data engineers, business analysts, and stakeholders, to drive data-driven decision-making.
Time Management: Strong organizational and time management skills to handle multiple projects, meet deadlines, and prioritize tasks effectively.
Continuous Learning: Eagerness to stay updated with the latest data analysis techniques, tools, and industry trends.
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