Data Analyst

Fresenius Medical Care is the world’s leading provider of products and services for individuals with renal diseases. As a global healthcare company, we have a special goal: to provide the best possible care. Join us to improve the quality of life for a growing number of patients around the world and be a vital part of our team.

Location: Remote (Spain-based, preferably Valencia). Full-Time Permanent Position

Required Years of Experience: 2 to 4

Middle Data Analyst:

As a Middle Data Analyst, you will turn data into actionable insights. With your experience, you'll bring fresh perspectives and a drive for continuous improvement. You will collaborate closely with stakeholders to design and develop interactive dashboards using Streamlit, Tableau, or Power BI, ensuring clarity and accessibility. Apply statistical methods to analyze data, uncover trends, and validate hypotheses, contributing valuable insights to decision-making processes.

Below you will find the type of work and tasks you will perform within the Data Solutions team.

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.

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.