Job Purpose:
The Data Analyst plays a crucial role in leveraging data to drive business insights, improve decision-making, and enhance operational efficiencies. Serving as a hub of skills, self-service tools, and knowledge, the Data Analyst aims to centralize expertise, standardize practices, and promote collaboration and innovation in data use. This role involves leading and fostering excellence in data foundations, including data storage, ingestion, governance, management, and analytics, establishing data as a seamless asset across the organization.
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.
