Job Description
As a Data Engineering intern, you will help build the data infrastructure that powers TI's AI/ML initiatives, analytics, and data-driven decision-making across the organization. You'll work with cutting-edge data technologies and cloud platforms, gaining hands-on experience in transforming raw data into actionable insights. You'll also have the opportunity to work in exciting areas like machine learning pipelines, big data processing, AI-driven analytics, cloud data architecture, real-time data streaming, and automated data workflows.
Responsibilities
- Assist in the development and maintenance of data pipelines and ETL/ELT workflows for processing datasets from multiple sources.
- Support the building and optimization of data models, schemas, and databases to ensure efficient data storage and accessibility.
- Participate in data cleaning, validation, and quality checks to help deliver accurate and reliable data for analytical use.
- Work with SQL, Python, and modern data tools such as Spark to support data flows and data science initiatives.
- Collaborate with data engineers and business teams to understand data requirements and contribute to solution development.
- Assist in monitoring data infrastructure performance and help troubleshoot issues as needed.
- Contribute to documentation for pipelines, data models, and transformation logic.
- Learn about emerging data technologies and support recommendations for data architecture improvements.
- Support the implementation of software engineering best practices such as testing and monitoring in data workflows.
Put your talent to work with us as a Data Engineering Intern!
Texas Instruments will not sponsor job applicants for visas or work authorization for this position.
Qualifications
Minimum Requirements
- Currently pursuing an undergraduate or graduate degree in Electrical Engineering, Computer Engineering, Computer Science, Data Science, or a related field.
- Cumulative 3.0/4.0 GPA or higher.
Preferred Qualifications
- Coursework or project experience with programming languages such as Python, Java, or SQL.
- Basic understanding of database concepts and data manipulation.
- Exposure to big data platforms (e.g., Spark), cloud services (AWS, Azure, or GCP), or machine learning concepts through coursework or personal projects.
- Ability to establish strong relationships with key stakeholders critical to success, both internally and externally.
- Strong verbal and written communication skills.
- Ability to quickly ramp on new systems and processes.
- Demonstrated strong interpersonal, analytical, and problem-solving skills.
- Ability to work in teams and collaborate effectively with people in different functions.
- Ability to take the initiative and drive for results.
- Strong time management skills that enable on-time project delivery.