Job Description

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)