Data Engineer

Texas Instruments
Dallas, TX, United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Computer Vision Automation of Tests Microsoft Azure Big Data Cloud Computing Databases Computer Engineering Continuous Integration Data Cleansing Information Engineering
+33 more
Data Governance Data Infrastructure Data Integrity Extract Transform Load (ETL) Dataspaces Apache Hadoop Python (Programming Language) Machine Learning Tensorflow Standard Sql Software Engineering SQL Databases Data Streaming Google Cloud Cloud Platform System Pytorch Delivery Pipeline Large Language Models Apache Spark Deep Learning HybridCloud Git Containerization Kubernetes Information Technology Integration Frameworks Data Management Machine Learning Operations GPT Software Version Control Data Pipelines Docker Programming Languages

Job description

At Texas Instruments, you will join a dynamic team where you can make a tangible impact on the future of technology. You will work on high-visibility projects, leveraging TI’s vast data ecosystem to drive innovation in AI, machine learning, and large-scale analytics.

As a Data Engineer, you will play a key role in building, maintaining, and optimizing the data infrastructure and systems that power AI/ML, analytics, reporting, and data-driven decision-making across the organization. You will work within cross-functional teams to transform raw, complex datasets into actionable insights, working closely with data scientists, software engineers, and business stakeholders.

In this role, you will move beyond foundational pipelines to assist in architecting and deploying large-scale data engineering solutions. You will contribute to the development of AI-driven data processing frameworks that integrate transformer-based LLMs, deep learning models, and traditional machine learning algorithms to solve complex industrial challenges., * Pipeline Development: Design, develop, and maintain scalable data pipelines and ETL/ELT workflows for ingesting and transforming large-scale datasets from diverse sources.

  • Data Modeling: Build and optimize robust data models, schemas, and databases to ensure high performance, accessibility, and data integrity.
  • Data Quality & Governance: Implement automated data cleaning, validation, and quality checks to ensure the delivery of highly reliable data for downstream analytical use.
  • Automation & Tooling: Utilize SQL, Python, and modern big data tools (e.g., Spark) to automate data flows and support advanced data science initiatives.
  • Cloud & Scale: Support the architecture and implementation of data engineering solutions across hybrid cloud environments (AWS, Azure, or Google Cloud Platform).
  • Engineering Excellence: Build reusable libraries and automated pipelines while applying rigorous software engineering best practices, including CI/CD, automated testing, and continuous monitoring.
  • Collaboration: Partner with analysts, engineers, and business leaders to translate complex data requirements into scalable technical solutions.
  • Infrastructure Management: Monitor data infrastructure performance, troubleshoot complex issues, and maintain comprehensive documentation for pipelines, models, and transformation logic.
  • Innovation: Stay at the forefront of emerging data technologies and recommend strategic upgrades to storage, processing, and architectural frameworks.

Requirements

  • Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, Data Science, or a related field.
  • 2+ years of professional experience in data engineering, software engineering, or a related technical role.

Preferred Qualifications:

  • Proven experience working with SQL and programming languages such as Python.
  • Advanced Degree: Master’s degree or PhD in a quantitative field (CS, EE, Data Science, etc.).
  • Big Data & Cloud: Hands-on experience with big data platforms (e.g., Spark, Hadoop) and cloud ecosystems (AWS, Azure, or Google Cloud Platform).
  • AI/ML Integration: Practical experience with AI/ML frameworks (e.g., PyTorch, TensorFlow) and an understanding of how to engineer data for LLMs, NLP, or computer vision.
  • Software Craftsmanship: Strong understanding of software engineering principles, including version control (Git), containerization (Docker/Kubernetes), and CI/CD pipelines.
  • Analytical Mindset: Strong problem-solving abilities with a track record of tackling multifaceted data challenges in a production environment.
  • Communication & Leadership: Ability to present technical findings and architectural designs to both technical peers and non-technical stakeholders.
  • Adaptability: Demonstrated ability to thrive in a fast-paced, evolving environment and take initiative to drive projects to completion.

About the company

Why TI?

  • Engineer your future. We empower our employees to truly own their career and development. Come collaborate with some of the smartest people in the world to shape the future of electronics.
  • We’re different by design. Diverse backgrounds and perspectives are what push innovation forward and what make TI stronger. We value each and every voice, and look forward to hearing yours. Meet the people of TI
  • Benefits that benefit you. We offer competitive pay and benefits designed to help you and your family live your best life. Your well-being is important to us. Please find our country-specific benefits here

About Texas Instruments

Texas Instruments Incorporated (Nasdaq: TXN) is a global semiconductor company that designs, manufactures and sells analog and embedded processing chips for markets such as industrial, automotive, data center, personal electronics and communications equipment. At our core, we have a passion to create a better world by making electronics more affordable through semiconductors. This passion is alive today as each generation of innovation builds upon the last to make our technology more reliable, more affordable and lower power, making it possible for semiconductors to go into electronics everywhere. Learn more at TI.com.

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