Data Engineer

Location: Dallas, TX, United States

Change the world. Love your job.

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.

Key Responsibilities:

• 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 GCP).
• 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.

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About the Organization