Principal Data Engineer

Job Posting

Title:

Principal Data Engineer

Hiring Department: Enterprise Technology – Data to Insights (D2I)

Position Open To: All Applicants

Weekly Scheduled

Hours:

40

FLSA Status: Exempt

Earliest

Start Date:

Immediately

Position Duration: Expected to Continue Until Dec 19, 2026

Location: Texas

The Principal Data Engineer for the UT Data Hub improves university outcomes and advances the UT mission to transform lives for the benefit of society by increasing the usability and value of institutional data. You will lead the data engineering team to innovate and implement the newest data engineering trends and best practices to create complex data pipelines within UT's cloud data ecosystem in support of academic and administrative needs.

You will leverage your creativity to solve complex technical problems and build effective relationships through open communication within the team and outside partners.

• Architect, design, and lead the development of enterprise-scale, production-grade data platforms and pipelines using Databricks and cloud-native technologies (AWS, Azure, or GCP).
• Champion the adoption of the Databricks Lakehouse architecture to unify data warehousing, data science, and machine learning workloads across the organization.
• Guide the design and deployment of AI‑ready data pipelines to support predictive analytics, generative AI, and advanced decision intelligence use cases.
• Define and enforce data engineering standards, including performance optimization, scalability, data observability, and cost efficiency.
• Oversee code reviews, architecture reviews, and system design discussions to ensure technical excellence and maintainability across the engineering team.
• Lead the implementation of robust data quality, governance, and compliance frameworks, leveraging Databricks Unity Catalog and modern metadata management tools.
• Solve complex data architecture and integration challenges using advanced technologies such as Spark, Delta Live Tables, Airflow, and MLflow.
• Drive the development of automated, CI/CD‑enabled data workflows and promote best practices in data infrastructure as code (IaC) and Dev Ops for data.

• Provide strategic technical leadership and mentorship to data engineering teams, fostering a collaborative environment that promotes innovation, accountability, and growth.
• Collaborate closely with data architects, AI/ML engineers, and analytics teams to align data solutions with organizational goals and research initiatives.
• Engage with cross‑campus and cross‑departmental technical groups to evangelize modern data practices and accelerate AI transformation initiatives.
• Lead knowledge‑sharing sessions and architecture reviews on emerging data engineering trends, Databricks advancements, and AI integration techniques.

• Effectively communicate technical strategies, project status, risks, and architecture decisions to both technical and non‑technical stakeholders.
• Translate complex data engineering concepts into clear business impacts, helping decision‑makers understand opportunities and trade‑offs.
• Produce clear and detailed technical documentation, design specifications, and operational playbooks to support long‑term scalability and training.
• Advocate for data engineering as a foundational enabler of AI, analytics, and digital transformation initiatives across the institution.

• Lead research and development efforts to evaluate and implement cutting‑edge technologies within the Databricks ecosystem and the broader AI/data landscape.
• Conduct feasibility studies and proofs of concept (POCs) for next‑generation architectures involving AI model integration, real‑time streaming, and intelligent automation.
• Partner with academic, administrative, and campus stakeholders to pilot AI‑enabled data systems, such as model‑assisted data validation and automated feature generation.
• Stay ahead of emerging trends in data engineering, AI readiness, and cloud infrastructure, continuously recommending and implementing innovative solutions.

• Contribute to recruitment, hiring, and onboarding of new data engineering team members.
• R…