Campus Undergraduate Full-Time Analyst

About The Team

The Enterprise Technology Services organization partners with every part of American Express to drive growth and innovation with trust and efficiency. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally, ensuring 24x7 support for an uninterrupted, high-quality experience. Our responsibilities include technology engineering, architecture, infrastructure, product management for core enterprise platforms, technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms.

About the Role

As an Analyst Data Analytics colleague in Enterprise Technology Services, you will join a full-time graduate program and support analytics work that helps technology teams understand portfolio performance, delivery progress, financial trends, resource utilization, and operational outcomes. This role sits at the intersection of technology, finance, portfolio management, reporting, and data analytics.

Key Responsibilities

• Collect, clean, validate, and organize data from portfolio, project, financial, operational, and technology delivery sources.
• Analyze portfolio performance, budget, forecast, resource, delivery, schedule, quality, and risk information to identify trends, insights, and areas for follow-up.
• Create and maintain dashboards, reports, key performance indicators, recurring analytics outputs, and leadership-ready summaries.
• Develop financial models, cost views, forecast summaries, scenario analyses, and variance insights.
• Partner with technology, finance, product, project, and business teams to understand reporting needs and translate them into analytical outputs.
• Support Software Development Lifecycle and Agile delivery reporting by analyzing delivery metrics, progress data, dependencies, and execution patterns.
• Prepare presentations, narratives, and stakeholder updates that communicate data-backed insights.
• Use Agentic AI, reporting, data analytics, and productivity tools for research, summarization, analysis, workflow efficiency, and human-validated outputs.

Requirements & Tech Stack

• Bachelor’s degree in Business Administration, Finance, Information Technology, Information Systems, Business Analytics, Data Analytics, Computer Science, Economics, or another relevant field by the full-time start date.
• Interest or experience in data analytics, financial management, portfolio reporting, technology delivery, business intelligence, or data-informed decision making.
• Foundational knowledge of data analytics concepts (data collection, validation, analysis, visualization, insight generation).
• Foundational understanding of financial management principles (budgeting, forecasting, cost tracking, variance analysis, financial modeling).
• Awareness of Software Development Lifecycle and Agile methodology concepts.
• Foundational understanding of Generative AI concepts (responsible use, appropriate use contexts, prompt-based workflows, human validation of AI generated outputs).
• Strong analytical thinking, attention to detail, communication, organization, problem solving, and collaboration skills.

Preferred Qualifications

• Expected graduation date between December 2026 and June 2027.
• Coursework, projects, internships, student organizations, or extracurricular experience related to data analytics, finance, business analysis, reporting, portfolio management, or technology delivery.
• Experience using analytical, reporting, or presentation tools such as Excel, PowerPoint, SQL, Python, Tableau, Power BI, or similar platforms.
• Exposure to financial modeling, dashboard development, key performance indicator reporting, forecast reconciliation, cost analysis, or operational analytics.
• Interest in technology portfolio management, project performance reporting, resource planning, process improvement, or business intelligence modernization.
• Familiarity with project or portfolio tools such as Jira, Rally, Confluence, SharePoint, Microsoft Project, or related workflow platforms.
• Coursework, projects, research, or internship exposure to data requirements, source-to-target mapping, data quality and controls, metadata, lineage, or data governance.
• Exposure to financial modeling, dashboard development, statistical analysis, scenario analysis, model documentation, data quality review, metadata, or data lineage concepts.
• Interest in AI governance, model risk management, responsible AI, explainability, enterprise data architecture, predictive analytics, or quantum computing research.
• Curiosity about Agentic AI reporting, AI enabled analytics, productivity tools, automated insight generation, and responsible validation of AI generated recommendations.
• Ability to build clear narratives from data, communicate findings clearly, and work effectively across finance, technology, architecture, risk, product, and business teams.