We invest heavily in new talent because fresh perspectives help us innovate and elevate what’s possible. As AI transforms how organizations use and manage their data, NetApp is helping shape the next generation of intelligent data infrastructure across cloud, hybrid, and on-premises environments.
As an intern, you’ll be encouraged to bring forward new ideas, take ownership of meaningful work, and drive results alongside a team that succeeds together, while receiving the mentorship, resources, and feedback you need to grow.
As a Data Analyst Intern, you’ll work alongside data, analytics, and business teams to transform complex data into meaningful insights. Depending on your team and project assignment, you may contribute to areas such as data analysis, data modeling, business intelligence, visualization, data quality, predictive analytics, automation, and AI-enabled analytics.
You’ll gain hands-on experience working with real business data while learning how analytics and emerging technologies can be used to solve problems, uncover opportunities, and support better decision-making.
What You’ll Do
- Collect, clean, validate, and analyze data from multiple sources.
- Use analytical methods to identify trends, patterns, anomalies, and opportunities.
- Query and analyze data using tools and languages such as SQL, Python, or R.
- Create dashboards, reports, and visualizations that make complex information easier to understand and act on.
- Develop or support data models, metrics, and reusable datasets.
- Assist with data quality, reconciliation, and root-cause analysis.
- Build or improve automated data preparation, analysis, or reporting workflows.
- Partner with business and technical teams to understand analytical needs and translate them into data solutions.
- Present insights and recommendations to technical and non-technical stakeholders.
Example Project Work
Intern projects vary based on team priorities and business needs and may include:
- Data Modeling & Business Intelligence: Create data models, metrics, or curated datasets that support dashboards and self-service analytics.
- Data Quality & Governance: Analyze data quality, identify discrepancies, validate metrics, and improve the accuracy and usability of data.
- Predictive Analytics: Apply statistical or machine-learning techniques to explore forecasting, segmentation, trends, or other business problems.
- Analytics Automation: Develop reusable scripts