About this role
Key Responsibilities
• Gain exposure to how large companies manage data across different areas like engineering, supply chain, finance, and manufacturing. • Learn how data is used to support decision‑making, dashboards, operations and reporting. • Assist with simple data quality checks or structured cleanup tasks under mentor guidance. • Work with senior IT leaders to understand high‑level data challenges and turn them into small, hands‑on prototype opportunities. • Collaborate on innovation initiatives that use modern data engineering, cloud capabilities, and predictive analytics to generate measurable business impact. • Learn how different systems (like SAP, cloud platforms, or analytics tools) connect/share data. • Learn what “data governance” means and why accuracy, consistency, and security matter in large organizations. • Help with documentation related to data definitions, business rules, or how a prototype handles data. • Learn foundational concepts about data security, classification, and why certain industries follow strict rules. • Follow security guidelines while working with data in prototypes or testing environments. • Gain early exposure to enterprise systems like SAP and cloud platforms so you can grow into more advanced data roles in the future. • Participate in learning sessions with senior leaders to understand how enterprise data supports major programs.
YOU MUST HAVE
• Bachelor’s degree (completed or in final year) in Computer Science, Data Science, IT, Engineering, or related technology discipline. • Ability to write code in at least one language (Python, SQL, Java, JavaScript/TypeScript). • Understanding of fundamental computer science concepts: algorithms, data structures, databases, debugging, and SDLC basics. • Exposure to at least one relevant area: web development, scripting/automation, cloud platforms, AI/ML, data analytics, or enterprise applications. • Strong problem‑solving skills, curiosity, willingness to learn fast, and ability to communicate. • Ability to work effectively with mentors, peers, and cross‑functional stakeholders. WE VALUE
• Hands‑on experience through academic projects, internships, capstones, or hackathons. • Exposure to Python data libraries, data cleaning, automation scripts, or analytics notebooks. • Experience with dashboards or BI tools (Power BI, Tableau, SAP Analytics Cloud). • Familiarity with cloud concepts (AWS/Azure fundamentals, APIs, IAM, serverless functions). • Exposure to AI/GenAI concepts (prompt engineering, embeddings, model evaluation, RAG). • Experience using Git, GitHub, Copilot, VS Code, CI/CD basics, or Agile tools such as JIRA. Technical Skills Programming & Scripting
• Basic Python skills for data analysis or simple automation • SQL fundamentals for querying and working with data • Optional exposure to Java or JavaScript • Optional basic scripting experience (PowerShell or Bash) Data & Analytics
• Understanding of how to clean, organize, and prepare data • Ability to build simple dashboards using tools like Power BI or Tableau • Exposure to basic analytics or introductory machine learning concepts Cloud & Data Platforms
• General awareness of cloud platforms (AWS, Azure, Snowflake) • Understanding of APIs at a beginner level • Basic knowledge of how data moves between systems Tools & Productivity
• Experience using Git or GitHub for version control • Familiarity with IDEs like VS Code • Exposure to work‑tracking tools (such as JIRA) • Basic troubleshooting skills (debugging simple code or data issues)