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.
