2027 Technology Summer Internship – Early Careers (Data Engineering)

Wells Fargo & Company•Iselin, NJ

1d•$38 - $63•Hybrid

About The Position

Wells Fargo is seeking aspiring Data Engineers to join its 2027 Technology Internship Program. This 10-week program immerses students in enterprise-scale data engineering, platform development, and modern software engineering practices. Interns help build and maintain data products, pipelines, and technology foundations that support business processes, applications, analytics, and AI capabilities. The program combines technical work with business context and emphasizes modern engineering principles, responsible AI adoption, data quality, and scalable technology solutions. Interns work on production-aligned data engineering and platform initiatives; gain exposure to enterprise data platforms, AI-enabled engineering practices, cloud technologies, and modern development workflows; and participate in structured training, mentorship, and networking with senior technology leaders. The program runs from June 2027 through August 2027. After successful completion, qualified candidates may be considered for a full-time position in the Technology Analyst Program the following year, in the same market as the internship. The role includes developing data pipelines, integration frameworks, data products, APIs, and services; applying software engineering practices; and supporting secure, reliable, and maintainable enterprise technology. Interns may use enterprise-approved generative AI tools and platforms to assist with coding, testing, data transformation, documentation, and prototyping, while following prompt engineering, data handling, confidentiality, governance, and Model Risk Management requirements. The position also supports data modeling, metadata management, data quality, monitoring, lineage, validation, governance, and enterprise data accessibility. The internship requires onsite presence on a hybrid schedule at one of the listed locations: Charlotte, North Carolina; Irving, Texas; or Iselin, New Jersey. Location placement depends on business needs, and any subsequent full-time Technology Analyst Program placement will align with the internship location. Candidates must pass a required technology skills assessment. The position is not eligible for visa sponsorship.

Requirements

At least 6 months of work experience, or equivalent demonstrated through work experience, training, military experience, or education.
Must take and pass the required technology skills assessment.
Must be presently authorized to work for any employer in the United States and not require Wells Fargo visa sponsorship now or in the future.

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, 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 coursework, research projects, internships, or other relevant experiences.
Basic understanding of data governance, data quality, metadata management, or regulatory compliance.
Strong problem-solving and interpersonal skills, including the ability to articulate technical challenges and recommend solutions.
Exposure to data visualization and reporting tools such as Tableau, Power BI, Grafana, SAS, or similar technologies.
Ability to work effectively independently and in collaborative, fast-paced environments.
Self-starter who takes initiative, acts with urgency, remains curious, and demonstrates a passion for learning and growth.
Involvement in extracurricular enrichment activities such as volunteerism, student organizations, study abroad, leadership positions, or nonprofit work.

Responsibilities

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 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, and product teams to deliver secure, reliable, and maintainable technology solutions.
Support platform modernization through engineering best practices, automation, and reusable frameworks.
Contribute to cloud-based and distributed computing platforms supporting enterprise-scale data workloads.
Use enterprise-approved generative AI tools and platforms for code development, refactoring, optimization, testing, data transformation, documentation, prototyping, and solution exploration within approved environments.
Apply prompt engineering 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 such as automated documentation, code review assistance, testing acceleration, and developer productivity tooling.
Support intelligent data capabilities, including anomaly detection, automated quality monitoring, metadata enrichment, and predictive insights, in alignment with MRM guidelines and regulatory requirements.
Explore emerging AI and machine learning technologies and their application to enterprise 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 focused on 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 practices for enterprise-scale data engineering, architecture, and platform operations.
Identify opportunities to improve operational insights and business outcomes through scalable engineering solutions.