Data Engineering Intern: AI/ML Data Pipelines

Texas Instruments; Dallas (TX); On-site; USD 27,552,000 - 41,328,000; Part time

Job summary

Texas Instruments is seeking a Data Engineering Intern to help build the data infrastructure powering TI's AI/ML initiatives, analytics, and data-driven decisions. You will gain hands-on experience transforming raw data into actionable insights using modern data tools and cloud platforms.

You'll work on machine learning pipelines, big data processing, real-time streaming, and automated data workflows, collaborating with data engineers and business teams while learning best practices for testing

Qualifications

• Pursuing a bachelor's degree in CS, Data Science, or a related field.
• Strong Python and SQL skills with coursework or project experience.
• Interest in data engineering, ETL processes, and cloud data platforms.

Responsibilities

• Develop and maintain data pipelines and ETL/ELT workflows.
• Model schemas and databases for efficient storage and access.
• Perform data cleaning, validation, and quality checks.
• Work with SQL, Python, and Spark on data flows.
• Collaborate with engineers and business teams on data requirements.
• Monitor data infrastructure performance and troubleshoot issues.
• Document pipelines, data models, and transformation logic.
• Learn emerging data technologies and propose architecture improvements.
• Support testing and monitoring in data workflows.

Skills

SQL

Python

Spark

ETL/ELT

Data modeling

Data quality

Cloud platforms

Data analytics

Education

Student in CS/Engineering or related field

Tools

Spark ecosystem

SQL tooling

Python data tools

Job description

Texas Instruments is seeking a Data Engineering Intern to help build the data infrastructure powering TI's AI/ML initiatives, analytics, and data-driven decisions. You will gain hands-on experience transforming raw data into actionable insights using modern data tools and cloud platforms.

You'll work on machine learning pipelines, big data processing, real-time streaming, and automated data workflows, collaborating with data engineers and business teams while learning best practices for testing