2027 Technology Summer Internship – Early Careers (Data Engineering)

Wells Fargo Bank•Charlotte, NC

1d•Hybrid

About The Position

Wells Fargo is seeking aspiring Data Engineers for its 2027 Technology Internship Program, a 10-week experience running from June 2027 through August 2027. Interns will work on production-aligned data engineering, platform development, and modern software engineering initiatives that support business processes, applications, analytics, and AI capabilities across the company. The program combines technical execution with business context and emphasizes modern engineering practices, responsible AI adoption, data quality, and scalable technology solutions. Participants receive structured training, mentorship, and networking opportunities with senior technology leaders. Qualified candidates who successfully complete the internship may be considered for a full-time position in the Technology Analyst Program the following year, in the same market as their internship. The role may involve designing and maintaining data pipelines and products; developing reusable APIs, services, and data solutions; working with cloud and distributed platforms; and applying software engineering practices such as testing, code reviews, version control, automation, CI/CD, and observability. Interns may also use enterprise-approved generative AI tools in engineering workflows, support intelligent data capabilities, and contribute to data reliability, governance, metadata, and architecture practices. The position requires onsite presence at one of the listed locations on a hybrid work schedule and requires passing a technology skills assessment. The position is not eligible for visa sponsorship. Wells Fargo will consider candidates presently authorized to work for any U.S. employer who will not require Wells Fargo visa sponsorship now or in the future. Internship locations are Charlotte, North Carolina; Irving, Texas; and Iselin, New Jersey. The posting lists location-specific hourly rates of $48.08 in Charlotte, $48.08 in Irving, and $60.10 in Iselin, alongside a stated base pay range of $38.46–$63.46 per hour. Pay may vary based on factors including performance, skills, experience, and work location; employees may also be eligible for incentive opportunities.

Requirements

At least 6 months of work experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination of these.
Present authorization to work for any employer in the United States without requiring Wells Fargo visa sponsorship now or in the future.
Ability to work onsite at one of the listed locations on a hybrid work schedule.
Ability to work independently and collaboratively in a fast-paced environment.
Strong problem-solving and interpersonal skills, including the ability to explain technical challenges and recommend solutions.
Proactive, curious self-starter with initiative, urgency, and a passion for learning and growth.
Involvement in extracurricular enrichment activities through one or more of volunteerism, student organizations, study abroad, leadership positions, or nonprofit work.

Nice To Haves

Currently pursuing a bachelor’s degree in Data Science, Computer Science, Computer Engineering, or a related STEM field, with expected graduation between December 2027 and June 2028.
Foundational knowledge of Python, SQL, data structures, algorithms, and object-oriented programming concepts.
Familiarity with software development lifecycle practices, including version control, testing, deployment, and CI/CD pipelines.
Exposure to AI tools or AI-assisted engineering workflows, such as Copilot, code assistants, or enterprise AI platforms.
Familiarity with prompt engineering, responsible AI principles, and AI governance practices.
Coursework or experience involving machine learning, artificial intelligence, statistical modeling, or advanced analytics.
Interest in AI’s impact on data engineering, software development, platform operations, and developer productivity.
Exposure to data engineering concepts such as data pipelines, distributed computing, data modeling, or large-scale data environments.
Understanding of cloud technologies, APIs, data services, and modern application architectures.
Experience with structured and unstructured data through coursework, research, internships, or other relevant experience.
Basic understanding of data governance, data quality, metadata management, or regulatory compliance principles.
Exposure to data visualization and reporting tools such as Tableau, Power BI, Grafana, SAS, or similar technologies.

Responsibilities

Design, develop, test, and maintain scalable data pipelines, integration frameworks, and data products for enterprise platforms.
Build and enhance reusable APIs, services, and data solutions for trusted and efficient data consumption across applications and business functions.
Apply software engineering practices, including testing, code reviews, version control, automation, CI/CD, and observability.
Develop solutions to ingest, transform, validate, and distribute large volumes of structured and unstructured data.
Partner with software engineers, architects, product teams, and business teams to deliver secure, reliable, maintainable technology solutions and improve data accessibility, discoverability, and usability.
Support platform modernization through engineering best practices, automation, and reusable frameworks.
Contribute to cloud-based and distributed computing platforms for enterprise-scale data workloads.
Use approved generative AI tools and enterprise AI platforms for code development, refactoring, optimization, testing, data transformation, documentation, prototyping, and solution design exploration.
Apply prompt engineering techniques and follow data handling, confidentiality, and governance standards when using AI-assisted tools.
Partner with senior engineers to review, validate, and govern AI-generated code, designs, and technical artifacts.
Contribute to AI-augmented engineering workflows, including automated documentation, code review assistance, testing acceleration, and developer productivity tooling.
Support intelligent data capabilities such as anomaly detection, automated quality monitoring, metadata enrichment, and predictive insights in line with Model Risk Management guidelines and regulatory requirements.
Explore emerging AI and machine learning technologies and their application to enterprise-scale data platforms and engineering ecosystems.
Support enterprise data models, metadata management processes, and data architecture standards.
Implement data quality controls, monitoring solutions, lineage capabilities, and validation frameworks.
Assist with data governance initiatives addressing data integrity, consistency, security, risk management, and policy adherence.
Contribute to standards and best practices for enterprise-scale data engineering, architecture, and platform operations.
Identify opportunities to improve operational insights and business outcomes through scalable engineering solutions.
Take and pass the required technology skills assessment.