Siemens Energy Internship 2026 – Logistics Data Analyst Intern
Siemens Energy
ExperienceFreshers / Students
QualificationB.E/B.Tech/B.Sc/M.E/M.Tech/M.Sc
Key Responsibilities
- Support the creation, enhancement and maintenance of Logistics KPI dashboards .
- Develop and maintain management reports using Power BI.
- Create clear data visualizations for operational reviews and management decision-making.
- Support project governance and reporting activities.
- Assist with data modelling and data preparation.
- Perform Excel-based data analysis.
- Support data quality corrections and validation.
- Collect, cleanse and validate logistics data.
- Monitor data quality and help maintain accurate reporting outputs.
- Analyze transport, warehouse and supply chain datasets .
- Identify trends, bottlenecks and opportunities for process improvement.
- Support Transport and Warehouse Performance Analytics initiatives.
- Contribute to Warehouse Analytics and Footprint Reporting activities.
- Work with business and IT teams to understand reporting requirements.
- Validate reporting requirements and align business definitions.
- Help ensure data accuracy and consistency.
- Support workshops, testing and User Acceptance Testing (UAT) .
- Work with stakeholders to understand analytics requirements.
- Document logistics data objects, business definitions and data models.
- Help maintain logistics master data documentation.
- Support data governance documentation.
- Prepare data products for business users.
- Maintain clear process and project documentation.
- Support project tracking and status updates.
- Track actions, milestones and follow-up activities.
- Maintain project documentation in project management tools.
- Assist project managers with logistics digitalisation initiatives.
- Prepare presentations, training materials and management reports.
Skills & Eligibility
- Candidates interested in the Siemens Energy Logistics Data Analyst Internship 2026 should meet the following requirements:
- Currently pursuing a bachelor’s or master’s degree .
- Relevant disciplines include Computer Science, Information Technology, Data Science, Business Analytics, Industrial Engineering, Supply Chain Management and related fields.
- Eligible qualifications include B.E, B.Tech, B.Sc, M.E, M.Tech and M.Sc.
- Strong analytical and problem-solving abilities.
- Curiosity and willingness to learn new technologies.
- Good communication and presentation skills.
- Ability to work independently and collaborate with international teams.
- Structured and detail-oriented approach to work.
- The role is focused on data analytics and reporting for global logistics operations. Candidates with knowledge of the following technologies and concepts can be relevant:
- Microsoft Excel: Advanced Excel knowledge is preferred.
- Power BI: Dashboard development, reporting and data visualization.
- SQL: Data extraction, querying and analysis.
- Data Analytics: Ability to identify trends, bottlenecks and improvement opportunities.
- Data Modelling: Understanding of relationships between datasets and business data structures.
- Reporting: Ability to create clear and useful management reports.
- Microsoft 365: Familiarity with Microsoft productivity and collaboration tools.
- Python: Python knowledge is desirable.
- Databases: Understanding of database concepts is useful.
- SAP: EWM, TM, ERP or S/4HANA.
- Snowflake: Knowledge of the cloud data platform.
- Process Mining: Exposure to tools such as Celonis.
- AI / Machine Learning: Basic understanding of AI and ML concepts.
- Data Governance: Understanding of data definitions, quality and governance practices.
- Practice presenting analytical findings using concise explanations, clear charts and business-focused recommendations.
- Candidates should currently be pursuing a bachelor’s or master’s degree in Computer Science, Information Technology, Data Science, Business Analytics, Industrial Engineering, Supply Chain Management or a related discipline. B.E, B.Tech, B.Sc, M.E, M.Tech and M.Sc qualifications are listed.
- Important skills include Microsoft Excel, Power BI, SQL, data analytics, reporting and data modelling . Python is desirable, while SAP, Snowflake, process mining and AI/ML knowledge are additional advantages.
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