As a COOP Student Data Analytics Analyst, reporting to the Data & Integration Architect, you will assist in analyzing data trends, creating reports, and supporting data-driven decision-making processes. You will work with various data sources, contribute to data visualization efforts, and ensure data integrity. Working closely with cross-functional business teams, you will gain hands-on experience with data analytics tools, statistical analysis, and business intelligence techniques. This role is ideal for students pursuing a career in data analytics, offering exposure to real-world data challenges and solutions.
Essential Functions
- Data Collection & Analysis
- Collect, clean, and preprocess data from various sources.
- Conduct exploratory data analysis to identify patterns and trends.
- Assist in generating business insights through data-driven methodologies.
- Reporting & Visualization
- Develop dashboards and reports using tools such as Power BI, Tableau, or Excel.
- Create visual representations of data to support decision-making.
- Present findings to stakeholders in a clear and concise manner.
- Statistical & Predictive Analysis
- Apply basic statistical methods to analyze datasets.
- Support predictive modeling and forecasting initiatives.
- Assist in identifying key performance indicators (KPIs) and measuring their effectiveness.
- Data Governance & Quality Assurance
- Ensure data accuracy and integrity through validation and quality checks.
- Assist in maintaining documentation on data processes and methodologies.
- Support compliance with data privacy and security policies.
- Collaboration & Cross-Functional Support
- Work with different teams to understand their data needs and provide analytical support.
- Assist in automating data workflows and optimizing data pipelines.
- Support ad-hoc data requests and business intelligence projects.
Competencies
- Working toward a degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
- Experience or coursework in SQL, Python, or R for data analysis.
- Familiarity with data visualization tools such as Power BI or Tableau, or Google Data Studio.
- Basic understanding of machine learning concepts and statistical analysis.
- Knowledge of relational databases and data querying techniques.
- Strong analytical and problem-solving skills.
- Ability to communicate insights effectively to non-technical audiences.
- Understanding cloud-based data services (Azure, Microsoft Fabric) is a plus.
