Data Analyst

Job Responsibilities Create and maintain interactive, user-friendly dashboards using Streamlit, Tableau, or Power BI tools. Collaborate with stakeholders to understand their requirements and translate them into effective visualizations. Implement best practices for data visualization, ensuring clarity, accuracy, and accessibility for a diverse audience. Stay updated on industry trends in data visualization and apply innovative techniques where appropriate. Statistical Analysis: Apply statistical methods to analyze data and extract meaningful insights. Utilize statistical models to validate hypotheses and support decision-making processes. Data Exploration and Insights: Conduct exploratory data analysis to uncover trends, patterns, correlations, and outliers. Provide actionable and strategic insights by analyzing complex datasets through compelling visualizations and narratives. KPI Development and Monitoring: Collaborate with business stakeholders to define and establish key performance indicators (KPIs) relevant to data analysis goals. Develop mechanisms for ongoing monitoring and reporting on KPI performance. Data Cleaning and Preprocessing: Clean and preprocess raw data to ensure accuracy and consistency in visualizations. Collaborate with data engineers to establish efficient data pipelines for visualization purposes. SQL for Data Access: Write and optimize SQL queries to extract and manipulate data from databases. Ensure efficient data retrieval for analysis and visualization purposes. Collaboration with Data Integration Teams: Work closely with data integration teams to ensure seamless data integration for visualization purposes. Provide input on data requirements for integration processes. Infrastructure and Azure Cloud Services: Leverage Azure cloud services for data storage, processing, and analysis. Collaborate with the infrastructure team to implement and optimize cloud-based solutions, ensuring scalability and efficiency. Provide input on infrastructure requirements for data storage, processing, and analysis. User Training and Support: Provide training sessions for end-users on accessing and interpreting visualizations. Offer ongoing support to users, addressing questions and refining visualizations based on feedback. Quality Assurance for Visualizations: Conduct thorough testing of visualizations to ensure accuracy, completeness, and responsiveness. Implement quality assurance processes for visual elements and data integrity. Documentation and Knowledge Sharing of Visualization Processes: Document the process of creating visualizations, including data sources, methodologies, and design choices. Maintain an organized repository of visual assets for future reference. Continuous Improvement, Learning, and Professional Development: Stay informed about advancements in data visualization tools and techniques. Continuously seek opportunities to enhance and optimize existing visualizations for improved decision-making. Stay updated on industry trends, new tools, and methodologies in data analysis. Participate in training programs and encourage a culture of continuous learning within the data team. Leadership and Mentorship: Lead and mentor junior-middle data analysts, providing guidance on best practices and fostering a collaborative team environment. #J-18808-Ljbffr