Data Engineer

Role Overview

As a Data Engineer at Texas Instruments, 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.

What You Will Do

You will design, develop, and maintain scalable data pipelines and ETL/ELT workflows for ingesting and transforming large-scale datasets from diverse sources, build and optimize robust data models, schemas, and databases, and implement automated data cleaning, validation, and quality checks.

Why It Might Be a Fit

You will contribute to the development of AI-driven data processing frameworks, work closely with data scientists, software engineers, and business stakeholders, and partner with analysts, engineers, and business leaders to translate complex data requirements into scalable technical solutions.

Requirements

• 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.

Benefits

• Competitive pay
• Benefits designed to help you and your family live your best life
• Country-specific benefits