DATA ANALYST

Following are the key responsibilities to be performed on daily basis in this Role.

Data Collection and Preparation:

• Collect and preprocess data from various sources including internal & external data sources, ensuring data quality and integrity for specific use cases.
  

Data must be Clean, transform, and wrangle to make it suitable for analysis.

Data Modeling & Visualization

• Build efficient data models in Power BI and develop intuitive dashboards and reports that enable stakeholders to gain insights.
• This includes utilizing DAX functions to perform calculations, rankings, and filtering.
  

Deployment and Integration:

• Deploy models into production systems, collaborating with software engineers.

• Integrate models into decision-making processes and applications.
  

Data Quality Assurance :

• Ensure data accuracy and reliability by identifying and resolving inconsistencies in the datasets.

• Use Power BI features to monitor data quality and establish best practices for data governance.
  

Stakeholder Communication

Communication and Visualization:

• Communicate findings and insights to non-technical stakeholders.
Create appealing data visualizations and reports using Power BI to present results effectively.

Administration

• Ability to manage and update the gateway, create connections (data sources), and manage user roles and access.

• Ensure secure and efficient data transfer between on-premises data sources and Power BI services.
  

Continuous Learning and Improvement:

4. TECHNICAL COMPETENCIES

An aspiring Data Analyst candidate requires a wide range of technical competencies to effectively analyze data, build data models, Design Appealing Dashboards and derive valuable insights. Here are some of the key technical competencies that are essential for a Data Analyst:

Data Manipulation and Cleaning:

• Proficient in collecting, cleaning, and preprocessing data from multiple sources, using Power Query for data transformation.

• Skilled in querying data from databases using SQL and integrating it into Power BI.
  

Data Modeling in Power BI:

• Expertise in building and optimizing data models within Power BI , focusing on efficient relationships, calculated columns, and measures using DAX (Data Analysis Expressions).

• Knowledge of star schema and snowflake schema modeling to improve performance and usability in Power BI reports.
  

Advanced DAX for Analytics:

• Mastery of DAX functions for advanced calculations, such as time intelligence, ranking, dynamic filtering, and performance optimization in reports.

• Experience in dynamic measures, KPIs, and predictive modeling within Power BI to support business forecasting.
  

Statistical and Analytical Thinking:

• Strong foundation in statistical concepts (e.g., hypothesis testing, regression analysis) applied through Power BI.

• Experience with R and Python integration within Power BI for more advanced statistical analysis and machine learning, if needed.
  

Optimization:

• Ability to optimize existing Dashboards performance to load the data effectively
  

Ethical Considerations:

Collaboration and Communication:

• Effective communication skills to collaborate with cross-functional teams and convey complex technical concepts to non-technical stakeholders.

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