Data Analyst

Roles and responsibilities

Translate business needs into analytics/reporting requirements to support executive decisions and workflows with required information.
Understand business user information needs and identify ways to prepare, visualize and present information in a user-friendly manner.
Act as a go-to-person for end users to interpret and use information. Provide knowledge sharing, mentoring, and training on analytics usage.
Work with business partners to understand data requirements.
Identify new opportunities to leverage data and to integrate data or applications to enhance business value.
Coordinate with data owners to ensure data accessibility and quality
Data Collection and Management
Data Gathering: Collect and compile data from various sources, including databases, spreadsheets, and external sources.
Data Cleaning: Ensure data quality by identifying and correcting errors, inconsistencies, and missing values.
Data Analysis
Statistical Analysis: Apply statistical techniques to analyze data sets and identify trends, patterns, and relationships.
Data Visualization: Create visual representations of data (charts, graphs, dashboards) to communicate findings clearly to stakeholders.
Reporting
Report Generation: Prepare regular reports and presentations summarizing insights, trends, and recommendations for management and other departments.
Ad-hoc Analysis: Conduct ad-hoc analysis as needed to address specific business questions or problems.
Collaboration
Cross-Departmental Collaboration: Work closely with other teams (e.g., marketing, finance, operations) to understand their data needs and provide analytical support.
Stakeholder Communication: Present findings and insights to stakeholders in a clear and actionable manner.
Tool Proficiency
Software Utilization: Utilize data analysis tools and software (e.g., SQL, Excel, Python, R, Tableau) to manipulate and analyze data effectively.
Database Management: Assist in maintaining databases and data warehouses, ensuring data integrity and accessibility.
Continuous Improvement
Process Optimization: Identify opportunities to improve data collection and analysis processes, enhancing efficiency and effectiveness.
Stay Updated: Keep abreast of industry trends, emerging technologies, and best practices in data analysis and analytics.
Problem-Solving
Critical Thinking: Employ critical thinking skills to interpret data results and provide actionable recommendations.
Data-Driven Decision Making: Support decision-making processes by providing accurate and relevant data insights.

Desired candidate profile

5+ Years of experience as Data Analyst/BI Analyst
Advanced knowledge in creating dashboards.
Advanced knowledge Paginated reports in Power BI report builder
Good understanding Data Warehousing concepts and data modelling

Technical Competencies

Power BI Dashboarding (Advanced)
Power BI Report Builder (Advanced)
Power BI Service (Advanced)
SQL (Oracle and SQL Server) (Intermediate)
Data Modelling (Advanced)
Analytical Skills:

Strong ability to analyze and interpret complex data sets, identifying trends, patterns, and insights.
Technical Proficiency:

Proficient in data analysis tools and software (e.g., SQL, Excel, Python, R, Tableau, Power BI) for data manipulation and visualization.
Statistical Knowledge:

Understanding of statistical methods and techniques for data analysis, including hypothesis testing and regression analysis.
Personal Attributes
Problem-Solving:

Strong critical thinking skills and the ability to approach data challenges creatively and effectively.
Communication Skills:

Excellent verbal and written communication skills to convey findings and insights to non-technical stakeholders clearly.
Detail-Oriented:

High attention to detail, ensuring accuracy and integrity in data analysis and reporting.
Additional Qualities
Collaboration:

Ability to work effectively in cross-functional teams, collaborating with stakeholders to understand data needs and deliver insights.
Adaptability:

Flexibility to adapt to changing priorities and new tools or technologies as needed.
Continuous Learning:

Commitment to ongoing professional development and staying updated on industry trends, tools, and best practices in data analytics