IT Analyst I

Responsibilities 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. Qualifications 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)