Data Analyst Intern

Job Summary

Net App’s Enterprise Architecture, Solutions & Intelligence (EASI) organization enables enterprise-wide transformation by combining architecture leadership, solution strategy, and data-driven intelligence. We partner across IT and business teams to modernize platforms, standardize metrics, and deliver trusted analytics. Our mission is to turn data into decisions using AI, Data, and Analytics—so leaders and teams can act faster with confidence.

As a Data Analyst Intern within EASI, you will help enable post-sales organizations with curated, governed data that supports decision-making ’ll coordinate with multiple business functions to understand their data needs, translate requirements into functional designs, create data models and semantic views, and contribute to modern experiences such as AI Agents and conversational analytics. This role blends stakeholder partnership, analytical thinking, and hands‑on data enablement.

• Coordinate with multiple post-sales business functions (e.g., Customer Success, Support, Professional Services) to understand goals, data needs, and reporting workflows.
• Participate in requirements workshops, clarify ambiguous requests, and drive alignment on KPI definitions and decision use‑cases.
• Communicate progress, dependencies, and trade‑offs clearly with both technical and non‑technical stakeholders.

• Convert business requirements into clear functional designs and delivery‑ready artifacts, including KPI/metric definitions, calculation logic, and business rules.
• Dashboard/report requirements (filters, drill paths, usability needs).
• Security and access requirements (e.g., role/region‑based visibility).
• Data refresh needs, data lineage assumptions, and validation plans.
• Acceptance criteria and test scenarios.
• Partner with data engineering / analytics engineering to confirm feasibility, reduce rework, and ensure scalable implementation.

• Data modeling & semantic views (Power BI enablement). Create and maintain data models aligned to post‑sales processes and analytics best practices (fact/dimension modeling, conformed dimensions where applicable).
• Standardized measures/KPIs. Business‑friendly naming conventions, hierarchies, and metadata.
• Reusable definitions and “single source of truth” datasets.
• Performance considerations and model usability patterns.
• Support data quality and metric integrity through reconciliation, anomaly checks, and root‑cause analysis of reporting discrepancies.

• Enable business functions with trusted datasets, semantic models, and documentation so they can build dashboards and visualizations in Power BI.
• Provide guidance on best practices for using shared datasets, consistent KPI interpretation, and governance expectations.
• Support adoption by creating how‑to documentation and lightweight enablement sessions as needed.

• Contribute to AI Agent and conversational analytics initiatives that allow stakeholders to ask questions in natural language and receive governed, explainable answers.
• Help define intents and analytic use‑cases, identify the right datasets/metrics to ground responses, and document guardrails (approved definitions, exclusions, confidence checks).
• Test and validate AI outputs for accuracy, consistency with metric definitions, and usefulness for business decisions.

• Work in an agile delivery model (stand‑ups, sprint planning, retrospectives) and maintain clear project documentation (requirements, designs, definitions, and change notes).
• Present insights, designs, and outcomes in a structured and actionable way.

Must be enrolled in an educational or professional program through summer 2026 or later.

Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors. Benefits may vary by country and region, and further details…