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Data Engineering Internship Jobs (NOW HIRING)

ARM, Inc.

Data Engineering Intern

San Clemente, CA · On-site

$17.75 - $23.25/hr

The Data Engineering Intern supports the development of scalable data systems and infrastructure ... This internship provides hands-on experience in building production-ready data systems within a ...

Qeexo, Co.

Data Engineering Intern

San Clemente, CA

$25/hr

The Data Engineering Intern supports the development of scalable data systems and infrastructure ... This internship provides hands-on experience in building production-ready data systems within a ...

Qeexo, Co.

Data Engineering Intern

San Clemente, CA · On-site

$25/hr

The Data Engineering Intern supports the development of scalable data systems and infrastructure ... This internship provides hands-on experience in building production-ready data systems within a ...

Gallo

Engineering Internship (January-June 2027)

Fresno, CA · On-site

$27.25/hr

  • Medical

  • Dental

  • Retirement

This internship is a full-time opportunity requiring a commitment of 40 hours per week). Gallo ... Work on Engineering teams to identify issues, collect appropriate supporting data, develop, and ...

Qeexo, Co.

Data Engineering Intern

San Clemente, CA · On-site

$25/hr

The Data Engineering Intern supports the development of scalable data systems and infrastructure ... This internship provides hands-on experience in building production-ready data systems within a ...

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How much do data engineering internship jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for data engineering internship in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What is a data engineering internship?

A Data Engineering Internship is a temporary position designed for students or recent graduates to gain practical experience in building, managing, and optimizing data pipelines and infrastructure. Interns typically work alongside professional data engineers to learn about data extraction, transformation, and loading (ETL) processes, database management, and data warehousing. This role provides hands-on exposure to tools like SQL, Python, and cloud platforms, helping interns develop essential technical skills for a career in data engineering. Interns may also assist in improving data quality and supporting analytics initiatives within a company.

What types of projects or tasks can I expect to work on during a data engineering internship?

As a Data Engineering Intern, you can expect to work on a variety of tasks such as building data pipelines, cleaning and transforming raw data, and assisting in the integration of data from multiple sources. You may also support the team in optimizing database performance and ensuring data quality. Interns often collaborate with data scientists, analysts, and software engineers, gaining exposure to different tools and technologies like SQL, Python, and cloud platforms. These experiences provide a strong foundation for a future career in data engineering.

What are the key skills and qualifications needed to thrive as a data engineering intern?

To thrive as a Data Engineering Intern, you need a solid grasp of programming (especially Python or Java), databases (SQL/NoSQL), and data structures, often demonstrated by coursework or relevant projects. Familiarity with data processing tools like Apache Spark, ETL pipelines, and cloud platforms such as AWS or Google Cloud is highly beneficial. Strong problem-solving abilities, attention to detail, and effective communication help interns excel in collaborative and fast-paced environments. These skills are crucial for building reliable data pipelines and supporting data-driven decision-making within organizations.

What is the difference between Data Engineering Internship vs Data Analyst Internship?

AspectData Engineering InternshipData Analyst Internship
Required SkillsSQL, Python, ETL, cloud platformsExcel, SQL, data visualization tools
Work EnvironmentData pipelines, backend systems, cloud infrastructureData reporting, dashboards, business insights
Industry UsageTech, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Data Engineering Internships focus on building and maintaining data pipelines and infrastructure, requiring technical skills like SQL and Python. Data Analyst Internships emphasize analyzing data to generate insights, often using Excel and visualization tools. Both roles are common in tech-driven industries but serve different functions within data teams.

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Data Engineering Internship jobs near you
Infographic showing various Data Engineering Internship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.

Data Engineering Intern

ARM, Inc.

San Clemente, CA • On-site

$17.75 - $23.25/hr

Other

Posted 5 days ago

Job description

TDK SensEI is transforming how sensor data is collected, processed, and leveraged powering intelligent, data-driven decision-making across
industrial environments. As a pioneer in automated machine learning for edge devices and a subsidiary of TDK Corporation, a global leader in sensor technology, SensEI operates at the forefront of industrial AI and analytics.
The Data Engineering Intern supports the development of scalable data systems and infrastructure that power our machine learning-based
equipment monitoring platform. In this role, the intern will work with large-scale sensor datasets, cloud-based technologies, and modern data
pipelines, while collaborating closely with software and machine learning engineers to enable advanced analytics and AI applications.
This internship provides hands-on experience in building production-ready data systems within a fast-paced, innovative environment.
KEY RESPONSIBILITIES
Build, maintain, and optimize ETL/ELT pipelines for processing sensor and operational data
Develop data workflows and automation using Python, SQL, and AWS services
Support data ingestion, transformation, validation, and monitoring processes
Work with structured and semi-structured data from cloud and edge-based systems
Collaborate with software and ML engineers to prepare datasets for analytics and machine learning models
Assist with integration and optimization of OLTP and OLAP systems
Troubleshoot pipeline issues and contribute to improvements in data reliability and performance
ADDITIONAL RESPONSIBILITIES
Create and maintain documentation, including data flows, system diagrams, and technical specifications
Participate in code reviews and adhere to engineering best practices
Support initiatives to improve data quality, observability, and operational efficiency
Contribute to continuous improvement of data infrastructure and workflows
Other Duties:
Perform other related duties and ad hoc projects as assigned to support departmental and organizational goals.
Workplace Safety: Maintain awareness of and follow all workplace safety guidelines and promote a culture of well-being.
Quality and Compliance: Ensure work is performed in accordance with established quality control and assurance processes.
Ethics and Integrity: Adhere to the company's Values and Code of Conduct and uphold the highest standards of honesty, integrity, and ethical behavior in all business activities.
QUALIFICATIONS
Education/Experience:
Currently pursuing a Bachelor's or Master's degree in:
Computer Science
Data Engineering
Information Systems
or a related technical field
Hands-on experience with Python and SQL
Exposure to data pipelines, ETL/ELT processes, or data warehousing concepts
Familiarity with relational and/or analytical databases (e.g., PostgreSQL, MySQL, Redshift)
Exposure to cloud platforms, preferably AWS
Knowledge/Skills/Abilities
Strong foundation in Python programming and SQL/database fundamentals
Basic understanding of data engineering concepts, including pipelines, transformation, and storage
Familiarity with cloud computing and distributed systems
Analytical mindset with strong problem-solving capabilities
Ability to quickly learn new tools, technologies, and frameworks
Strong attention to detail and commitment to data accuracy and quality
Effective communication skills, both written and verbal
Ability to collaborate in cross-functional team environments
Preferred / Bonus Qualifications
Experience with workflow orchestration tools (e.g., Apache Airflow)
Familiarity with AWS services such as S3, Lambda, Glue, or Redshift
Exposure to Docker, Linux, or shell scripting
Understanding of OLTP vs. OLAP systems
Experience with data lakes, data warehousing, or analytics platforms
Interest in machine learning and data-driven systems
Experience with Git or version control systems

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