Data & AI Engineer Intern

Main Duties

As a Data & AI Engineer Intern at COS, you will support and work in collaboration with the wider COS AI, Analytics & Data team. You will assist in identifying strategic challenges, conducting in-depth analysis, building of data products and collaborating with stakeholders to turn data into actionable insights that support business objectives.

Please be considerate in your application choice and only apply to the internship opportunity that best suits your skillset and desired career path.

What you will do:

• Design, build, and maintain ETL pipelines to ensure efficient data flow and integration.
• Transform and model data using SQL and dbt to support analytical needs.
• Provide data support for dashboards and reports, ensuring accuracy and reliability.
• Develop and evaluate machine learning and statistical models to derive insights and predictions.
• Validate data quality and troubleshoot issues to maintain data integrity.
• Manage and optimize databases (e.g., PostgreSQL, BigQuery) for performance and scalability.
• Document data models and business metrics to ensure clarity and consistency.
• Collaborate with cross-functional teams including engineers, analysts, and stakeholders to meet business objectives.

Alignment to our company values is the most important characteristic we look for in all new joiners. Our values are the behaviours that we appreciate above and beyond anything else. We are open-minded and curious, we dare to be different, we believe in constant improvement and we empower and trust you to take ownership. Our values are part of who we are, what we stand for and how we act.

What you need to succeed:

• Analytical and problem-solving mindset to tackle complex challenges.
• Strong teamwork and communication skills for effective collaboration.

Preferred:

• Experience with dbt, BI tools, Git, or cloud platforms.
• Previous involvement in data/analytics projects.

What will the internship offer the student and what will they learn while they are with you? E.g. skills they could learn/develop and experience they could gain.

Learning Outcomes:

• Proficiency in SQL and Python for data manipulation and analysis.
• Hands-on experience with dbt and advanced data modeling techniques.
• Introduction to machine learning modeling and evaluation.
• Comprehensive understanding of data pipelines and ETL processes.
• Familiarity with BI tools (e.g., Looker, Tableau, Power BI) for data visualization.
• Exposure to cloud platforms (e.g., Azure, GCP) and their data services.
• Knowledge of version control and data testing practices.
• Ability to communicate technical insights effectively to various stakeholders.