Summary: As a Senior Business Data Analyst, you will be responsible for acquiring, cleaning, analyzing, mining, and visualizing business data. You will utilize your skills in data analysis and data visualization to perform end-to-end data management and analysis, from data collection/preparation to model implementation and business insights. Effective communication with various business teams to accurately analyze and understand requirements is crucial. You will create data dashboards that align with business needs, demonstrating proficiency in data visualization and strong data processing skills using tools such as Excel, VBA, Power Query, SQL, R, SPSS, Python, etc. Proficiency in data analysis tools like PowerBI, Tableau, and Metabase, as well as familiarity with Python and common libraries (Pandas/Numpy), and experience using Jupyter Notebook are required.
Responsibilities: 1. Collect, clean, organize, and process business data to ensure accuracy and completeness. 2. Perform data analysis and mining to provide business insights and decision support. 3. Develop and maintain data visualization reports and dashboards to showcase data analysis results and insights. 4. Collaborate with various business teams to understand their requirements and provide data analysis solutions accordingly. 5. Conduct end-to-end data management and analysis to ensure efficient and accurate data flow. 6. Continuously learn and explore new data analysis tools and techniques to enhance data analysis capabilities and efficiency.
Qualifications: 1. Minimum of 5 years of experience in data analysis or related fields. 2. Experience in handling and analyzing large-scale data, capable of processing and analyzing complex business data. 3. Successful experience in data visualization and report development. 4. Proficiency in data analysis and processing tools such as Excel, VBA, Power Query, SQL, R,SPSS, Python, etc. 5. Familiarity with data analysis tools like PowerBI, Tableau, and Metabase, with data visualization and report development capabilities. 6. Proficiency in Python and its common libraries (Pandas, Numpy), and familiarity with Jupyte Notebook for data analysis and model implementation.
