Role Description The Analytics Engineer Intern supports the data and analytics team in transforming raw data into meaningful, actionable insights. This role is designed to provide hands-on experience with modern data tools and practices, including data transformation, modeling, and visualization. The intern will work closely with experienced team members to assist in building data pipelines, maintaining data quality, and creating basic reports and dashboards that support operational and business decision-making. This position emphasizes learning, collaboration, and professional development in a real-world environment. Duties & Responsibilities - Assist in building and maintaining data pipelines under guidance from senior team members - Support data cleaning, transformation, and validation processes to ensure data accuracy - Help document data sources, transformations, and workflows - Work with team members to understand basic business requirements and translate them into data tasks - Assist in developing simple data models (e.g., staging, fact, and dimension tables) - Support creation and maintenance of dashboards and reports (e.g., Tableau or similar tools) - Participate in team meetings, code reviews, and knowledge-sharing sessions - Collaborate with cross-functional teams including IT, HR, Finance, and Operations - Learn and apply best practices in analytics engineering, data modeling, and data governance Qualifications - No prior professional experience required - Academic or personal projects involving data are strongly preferred - Basic understanding of SQL or willingness to learn - Familiarity with Excel or similar data tools - Interest in data engineering, analytics, or business intelligence - Exposure to or willingness to learn: dbt, Snowflake or cloud data platforms, Tableau or other data visualization tools - Strong problem-solving and analytical thinking skills - High attention to detail and data accuracy - Strong communication skills and willingness to ask questions - Ability to work both independently and in a collaborative team environment - Demonstrated curiosity and passion for learning new technologies Requirements - Currently pursuing a Bachelor’s or Master’s degree in Information Systems, Computer Science, Data Analytics, or related field (Junior-level coursework completed).

• Design, develop, and maintain high-quality dashboards and reports in Power BI tailored to business needs. • Perform efficient data queries and modeling in SQL and Databricks, ensuring data integrity and performance. • Collaborate on data integration and governance processes across multiple data sources. • Interact with sales, operations, and support teams to understand their processes and translate them into actionable BI solutions. • Support automation and optimization initiatives for reporting to minimize operational overhead. • Document data processes, business definitions, and reporting best practices.

• Take ownership of meaningful metrics and data products, from definition through adoption. • Build and maintain dbt models that define how we measure the business. • Design and maintain dashboards used by growth, finance, and operations to make day-to-day decisions. • Improve data quality through testing, monitoring, and validation. • Enable self-serve analytics so teams can answer their own questions and move faster.

• Structure and evolve the corporate analytics layer, transforming data already available on the platform into reliable, organized, metrics-oriented assets. • Analytics Modeling & Metrics: Develop and evolve dimensional and metric-driven models. • Define and standardize corporate KPIs. • Build data marts oriented to business domains. • Define and maintain a semantic layer for consumption by BI tools and analytical applications. • Document business rules and metric calculations. • Solution and Data Product Development: Design and evolve data products based on certified metrics. • Create analytics-ready datasets for consumption. • Establish reliable data foundations to support AI initiatives and predictive models. • Collaborate with business areas in designing data-driven solutions. • Quality, Governance & Performance: Implement quality tests in the analytics layer. • Ensure traceability and consistency of metrics. • Ensure adequate performance for analytical consumption. • Strategic Engagement: Work closely with Product, Operations and Finance to define strategic metrics.