Roles & Responsibilities:

1. Requirement Gathering

• Interact with business users to understand their analytical and reporting needs.
• Validate and confirm requirements through meetings, interviews, and workshops.
• Document requirements clearly and obtain formal sign-off from stakeholders.

2. Solution Design and Data Preparation

• Design data solutions that meet business requirements and align with best practices.
• Collaborate with data engineers to define data pipelines and acquire data from various sources.
• Coordinate with stakeholders to collect non-system data, including spreadsheets and external datasets.
• Cleanse and preprocess data to ensure accuracy, completeness, and consistency.
• Conduct rigorous data validation and quality assurance checks.

3. Data Analysis and Interpretation

• Transform and model data to facilitate analysis.
• Conduct detailed data analysis to uncover trends, patterns, and insights.
• Utilize statistical methods and data visualization tools to effectively communicate findings.
• Interpret results to provide actionable business recommendations.

4. Reporting and Visualization

• Develop comprehensive reports and interactive dashboards to present key insights.
• Create clear and effective data visualizations (charts, graphs, etc.) to communicate complex information.
• Implement automation for routine reporting tasks to increase efficiency and accuracy.

5. Data Modeling and Predictive Analytics

• Develop and maintain predictive models to enhance forecasting and support data-driven decision-making.
• Employ statistical and machine learning techniques for predictive analytics and deeper insights.

6. Data Governance and Compliance

• Ensure compliance with data privacy and security regulations in all data-related activities.
• Contribute to establishing and maintaining data governance best practices for BI tools.
• Work with data stewards to define and uphold data quality standards.

7. Stakeholder Collaboration

• Partner with business units and functional expert to understand their data requirements and objectives.
• Present data findings and insights to non-technical stakeholders in an understandable manner.
• Facilitate communication between technical and non-technical teams to ensure alignment.

8. Continuous Learning and Skill Development

• Keep abreast of the latest trends and advancements in data analysis and data science.
• Continuously develop technical and analytical skills to enhance data analysis capabilities.

9. Documentation

• Create and maintain comprehensive documentation for business requirements, functional specification, technical specification, etc.
• Ensure documentation is up-to-date, accurate, and accessible to relevant stakeholders.
• Develop user guides and training materials to support the adoption of data tools and processes.

KPI Quantitative:

• Accuracy and completeness of data analyses.
• Timeliness of reports and insights delivery.
• Adoption and usage of data-driven insights by stakeholders.
• Data quality and integrity metrics.

KPI Qualitative:

• Effectiveness of data visualizations and reports in conveying information.
• Quality of recommendations provided to improve decision-making.
• Feedback from stakeholders on the value of data analysis contributions.

Internal Relationships:

• Cross-functional teams and departments.
• Data engineers and data scientists.
• Data stewards and data governance teams.

External Relationships:

• Collaborate with external vendors and consulting services.

Qualifications:

• Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, or a related discipline.
• Minimum of 5 years of experience in data analysis, business intelligence, or a related field.
• Proven track record of leveraging data to drive business insights, improve decision-making, and enhance operational efficiencies.
• Excellent communication and collaboration skills, with the ability to present findings to non-technical stakeholders and work effectively with cross-functional teams.
• Proficiency in English for writing and communication.
• Proficiency in data analysis tools and programming languages, including SQL and Python. Knowledge of R is a plus.
• Strong expertise in data visualization tools such as Power BI. Experience with Looker is a plus.
• Experience in setting up report security and access control in Power BI report and dashboard.
• Experience with data governance and data management practices.
• Experience with managing Power BI premium capacity and assets.
• Familiarity with data storage, ingestion, and management technologies.
• Familiarity with data modeling, predictive analytics, and machine learning techniques.
• Relevant certifications such as Microsoft Certified: Data Analyst Associate, Google Data Analytics Professional Certificate, or other recognized industry certifications are a plus.
• Commitment to continuous learning and staying updated with the latest trends and advancements in data analysis and data science.
• Strong analytical and problem-solving skills, with the ability to tackle complex data challenges and deliver actionable insights.