Dashboard Design and Development:
* 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.
Data Visualization Best Practices:
* 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 to middle data analysts, providing guidance on best practices and fostering a collaborative team environment.
#J-18808-Ljbffr