Career Accelerator Program - Data Engineer

Texas Instruments•Dallas, TX

About The Position

Change the world. Love your job. In your first year with TI you'll join the Career Accelerator Program (CAP) – a development experience that blends professional skill workshops, technical training and on the job learning. The program is tailored to your educational background and experience level, ensuring you can start delivering real world data engineer impact from day 1, whether you're working on foundational data pipelines or cutting-edge AI-driven solutions. About the job: As a Data Engineer, you will play a key role in building and maintaining the data infrastructure and systems that power AI/ML, analytics, reporting, and data-driven decision-making across the organization. You will be part of a cross-functional team, gaining hands-on experience working with modern data tools and cloud technologies while transforming raw data into actionable insights through collaboration with engineers and business stakeholders. In this role, you will also have the opportunity to architect and lead the deployment of large-scale data engineering solutions, pioneering AI-driven data processing frameworks that integrate transformer-based LLMs, deep learning models, and traditional machine learning algorithms.

Requirements

Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, Data Science, or related technical field of study
Cumulative 3.0/4.0 GPA or higher
Texas Instruments will not sponsor job applicants for visas or work authorization for this position.

Nice To Haves

Master's degree and/or PhD in Electrical Engineering, Computer Engineering, Computer Science, Data Science, or related field
Proficiency in programming languages such as Python, Java, Scala, C/C++ and strong SQL skills for data manipulation and querying
Understanding of ETL processes, database concepts, and experience with big data platforms (e.g., Spark), cloud services (AWS, Azure, or GCP)
Experience with AI/ML frameworks (e.g., PyTorch) and large-scale data processing, including transformer-based LLMs and neural networks
Knowledge of machine learning algorithms ranging from traditional ML to cutting-edge deep learning models
Strong analytical and problem-solving abilities with experience tackling complex, multifaceted challenges
Exposure to or proven experience in machine learning, deep learning concepts, NLP, computer vision, speech, and time series analysis
Demonstrated ability to develop end-to-end data pipelines, AI-enabled data tools, or enterprise-scale data architecture solutions
Publications or conference presentations in AI/ML or data engineering topics
Proven teamwork and communication skills in multidisciplinary projects, including ability to present technical concepts to non-technical stakeholders
Strong time management and project management skills that enable on-time delivery of high-impact projects
Demonstrated ability to build strong, influential relationships and leadership in cross-functional team environment
Ability to work effectively in a fast-paced and rapidly changing environment with strong initiative and adaptability
Ability to take initiative, drive for results, and drive innovation

Responsibilities

Develop and maintain scalable data pipelines and ETL/ELT workflows for ingesting, processing, and transforming large datasets from multiple sources
Build and optimize data models, schemas, and databases to ensure efficient data storage, accessibility, and performance
Perform data cleaning, validation, and quality checks to deliver accurate and reliable data for analytical use
Work with SQL, Python, and modern data tools such as Spark to automate data flows and support data science initiatives
Architect and implement large-scale data engineering solutions across hybrid cloud environments
Build reusable libraries and automated pipelines while applying software engineering best practices such as CI/CD, testing and monitoring
Collaborate with analysts, engineers, and business teams to understand data requirements and deliver solutions
Monitor data infrastructure performance and troubleshoot issues as needed
Maintain documentation for pipelines, data models, and transformation logic
Forecast emerging data needs, define design standards and drive strategic upgrades to storage and processing infrastructure
Stay updated on emerging data technologies and recommend improvements to data architecture