2027 Technology Summer Internship – Early Careers (Data Engineering)

Location: CHARLOTTE, NC; IRVING, TX; ISELIN, NJ

About this role

Wells Fargo is seeking aspiring Data Engineers to join the 2027 Technology Internship Program.

Program Overview

The Wells Fargo Technology Internship Program provides participants with a 10-week experience designed to immerse students in enterprise-scale data engineering, platform development, and modern software engineering practices. Participants will help build and maintain data products, pipelines, and technology foundations that power critical business processes, applications, analytics, and AI capabilities across Wells Fargo. The program blends technical execution with business context, emphasizing modern engineering principles, responsible AI adoption, data quality, and scalable technology solutions.

Participants will

Work hands-on with impactful, production-aligned data engineering and platform initiatives

Gain exposure to enterprise data platforms, AI-enabled engineering practices, cloud technologies, and modern development workflows

Participate in structured training, mentorship, and networking opportunities with senior technology leaders

Upon successful completion of the internship, qualified candidates may be considered for a full-time position in the Technology Analyst Program the following year, within the same market where the internship was completed. #earlycareers

In this role, your responsibilities as a Data Engineering Intern may include:

Data Engineering & Platform Development

Design, develop, test, and maintain scalable data pipelines, integration frameworks, and data products supporting enterprise platforms

Build and enhance reusable APIs, services, and data solutions that enable trusted and efficient consumption of data across applications and business functions

Apply software engineering fundamentals including testing, code reviews, version control, automation, CI/CD, and observability practices

Develop solutions to ingest, transform, validate, and distribute large volumes of structured and unstructured data

Partner with software engineers, architects, and product teams to deliver secure, reliable, and maintainable technology solutions

Support platform modernization efforts through engineering best practices, automation, and reusable frameworks

Contribute to cloud-based and distributed computing platforms supporting enterprise-scale data workloads

AI-Enabled Data Engineering & Modern Development Practices

Leverage enterprise-approved generative AI tools (e.g., coding agents) and enterprise AI platforms to accelerate engineering activities, including:

Code development, refactoring, and optimization

Automated testing and validation

Data transformation and pipeline development

Technical documentation generation

Rapid prototyping and solution design exploration within approved environments

Apply prompt engineering techniques to improve the quality, reliability, and efficiency of AI-assisted outputs while adhering to data handling, confidentiality, and governance standards

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 development of intelligent data capabilities such as anomaly detection, automated quality monitoring, metadata enrichment, and predictive insights in alignment with Model Risk Management (MRM) guidelines and regulatory requirements

Explore emerging AI and machine learning technologies and their application within enterprise-scale data platforms and engineering ecosystems

Data Reliability, Governance & Business Enablement

Support development and maintenance of enterprise data models, metadata management processes, and data architecture standards

Implement data quality controls, monitoring solutions, lineage capabilities, and validation frameworks to improve trust and reliability

Assist with data governance initiatives focused on data integrity, consistency, security, risk management, and policy adherence

Partner with technology and business teams to improve accessibility, discoverability, and usability of enterprise data assets

Contribute to standards and best practice for enterprise-scale data engineering, architecture, and platform operations

Assist in identifying opportunities to improve operational insights and business outcomes through scalable engineering solutions

Program Duration: June 2027 – August 2027 (10 weeks)

Required Qualifications

6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications

Currently pursuing a bachelor's degree in Data Science, Computer Science, Computer Engineering, or a related STEM field with an 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

Demonstrated exposure to AI tools or AI-assisted engineering workflows (e.g., Copilot, code assistants, enterprise AI platforms)

Familiarity with prompt engineering concepts, responsible AI principles, and AI governance practices

Experience or coursework involving machine learning, artificial intelligence, statistical modeling, or advanced analytics concepts

Interest in how AI is transforming data engineering, software development, platform operations, and developer productivity

Exposure to data engineering concepts including data pipelines, distributed computing, data modeling, or large-scale data environments

Understanding of cloud technologies, APIs, data services, and modern application architectures

Experience working with structured and unstructured data through