Career Accelerator Program - Data Engineer

Edbz; Nebraska; On-site; USD 60,000 - 85,000; Full time

Job summary

Texas Instruments is seeking new graduates to join the TMG Development program and Career Accelerator Program (CAP). You will receive thorough training across professional and technical areas to accelerate your ramp into TI and support long-term career growth.

The 12-month TMG Development program focuses on designing, building and operating data architectures and data pipelines using modern tools and cloud/on-prem storage. You will gain hands-on experience from day one.

Qualifications

• Bachelor's degree in Electrical Engineering or Computer Engineering or related field; GPA >= 3.0.
• Strong foundation in data engineering concepts and programming.

Responsibilities

• Designs and maintains data architecture and software.
• Develops tools leveraging AI/ML and big-data techniques.
• Ensures data integrity and storage optimization.
• Reviews requirements and suggests system upgrades.

Skills

Python/Java/Scala

SQL

Analytical thinking

Problem solving

Big data concepts

Team collaboration

Education

Bachelor's degree in EE/CE

Tools

Apache Spark

Kafka

Iceberg

S3/MinIO

Job description

Change the world. Love your job.

In your first year with TI, you will participate in the Career Accelerator Program (CAP), which provides professional and technical training and resources to accelerate your ramp into TI, and set you up for long‑term career success. Within this program, we also offer function-specific technical training and on-the-job learning opportunities that will encourage you to solve problems through a variety of hands‑on, meaningful experiences from your very first day on the job. The TMG Development program is a 12‑month program for new college graduates in the TMG organization.

About the job:

Designs, builds and oversees the deployment and operation of technology architecture, solutions and software to capture, manage, store and utilize structured and unstructured data from internal and external sources. Establishes and builds processes and structures based on business and technical requirements to channel data from multiple inputs, route appropriately and store using any combination of distributed (cloud) structures, local databases, and other applicable storage forms as required. Develops technical tools and programming that leverage artificial intelligence, machine learning and big‑data techniques to cleanse, organize and transform data and to maintain, defend and update data structures and integrity on an automated basis. Creates and establishes design standards and assurance processes for software, systems and applications development to ensure compatibility and operability of data connections, flows and storage requirements. Reviews internal and external business and product requirements for data operations and activity and suggests changes and upgrades to systems and storage to accommodate ongoing needs.

Minimum Requirements:

• Bachelor's degree in Electrical Engineering, Computer Engineering or related field of study
• Minimum cumulative GPA 3.0/4.0

Preferred Qualifications:

• Proficiency in at least one programming language commonly used in data engineering (Python, Java, or Scala)
• Solid understanding of SQL and relational database concepts
• Coursework or project experience involving data structures, algorithms, and software design fundamentals
• Strong analytical and problem‑solving skills, with the ability to learn new tools and technologies quickly in a fast‑paced environment
• Exposure to distributed data processing frameworks such as Apache Spark
• Familiarity with streaming/event‑driven architectures (e.g., Apache Kafka)
• Understanding of modern data lake/lakehouse concepts, including table formats like Apache Iceberg and object storage systems (e.g., MinIO, S3)
• Experience with SQL query engines for large‑scale analytics (e.g., Trino/Presto)
• Internship, co‑op, academic research, or personal project experience involving big data pipelines, ETL/ELT workflows, or cloud/on‑prem data infrastructure