Principal Data Engineer

* Competitive health benefits (Employee premiums covered at 100%; family premiums at 50%)* Vision, dental, life, and disability insurance options* Paid vacation, sick leave, and holidays* Teachers Retirement System of Texas (a defined benefit retirement plan)* Additional voluntary retirement programs: tax sheltered annuity 403(b) and a deferred compensation program 457(b)* Flexible spending account options for medical and childcare expenses* Training and conference opportunities* Tuition assistance* Athletic ticket discounts* Access to UT Austin's libraries and museums* Free rides on all UT Shuttle and Capital Metro buses with staff ID card* 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.* Contribute to recruitment, hiring, and onboarding of new data engineering team members.* Represent the data engineering function in strategic planning discussions and cross-organizational technology initiatives.* Perform other duties as assigned, aligned with the mission to build a secure, scalable, and AI-enabled data ecosystem.* Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.* 5+ years of experience designing, implementing, and maintaining complex, production-grade data pipelines and enterprise data platforms.* 5+ years of hands-on experience with cloud-based data engineering, preferably in Amazon Web Services (AWS), with strong command of services such as Glue, S3, Lambda, Redshift, and EMR.* 3+ years of experience defining cloud data architecture and data strategy in large, distributed enterprise environments.* Deep expertise with Databricks Lakehouse Platform, including Delta Lake, Delta Live Tables, and Unity Catalog, for scalable data ingestion, transformation, and governance.* Proficiency in Python, PySpark, and SQL, with demonstrated experience in building ETL/ELT workflows across structured and unstructured data sources.* Proven ability to design and implement high-performance, AI-ready data architectures supporting analytics, machine learning, and real-time data processing.* Experience developing and deploying Continuous Integration / Continuous Delivery (CI/CD) pipelines for data engineering using tools such as Databricks Repos, GitHub Actions, or Terraform.* Strong foundation in test-driven data engineering, including automated data quality, validation, and observability frameworks.* Advanced knowledge of data governance, metadata management, and security compliance in cloud and Databricks environments.* Excellent systems analysis, design, and troubleshooting skills with the ability to address performance bottlenecks in distributed data systems.* Exceptional communication skills, with the ability to convey complex technical concepts clearly to both technical and non-technical stakeholders.* Proven experience leading and mentoring teams, fostering technical excellence and innovation.* Self-motivated and capable of working independently in a dynamic, evolving technology landscape.* 10+ years of experience in Data Engineering, Data Architecture, or related fields, including 5+ years of hands-on work with Databricks or equivalent large-scale data platforms.* Demonstrated experience architecting and optimizing Lakehouse environments that integrate data science, analytics, and AI workloads.* Proven success in implementing AI/ML-ready data pipelines and collaborating with Data Scientists and MLOps teams using tools like MLflow, Feature Store, or model registries.* 5+ years of experience applying Agile software development methodologies and using tools such as JIRA, Confluence, or Azure DevOps for project tracking and delivery.* Expertise in distributed data processing and streaming technologies such as Apache Spark, Kafka, Flink, or Airflow for orchestration and automation.* Experience designing and operationalizing data observability and cost optimization strategies within Databricks and cloud environments.* Strong understanding of data mesh, data fabric, and modern metadata management principles for large-scale organizations.* Professional certifications such as Databricks Certified Data Engineer Professional, AWS Solutions Architect, or AWS Data Analytics Specialty are highly desirable.* Demonstrated ability to drive innovation, introduce emerging technologies, and lead proofs of concept (POCs) for AI integration, automation, or advanced analytics.* Commitment to continuous learning and technology leadership, staying current with advancements in Databricks, AI engineering, and modern cloud data ecosystems.**Start Here, Change the World**At The University of Texas at Austin, tradition meets innovation in the heart of a city that frequents lists of the best places to live and work. Named by Forbes as one of America's Best Large Employers for the sixth year in a row in 2025, UT offers both a dynamic work environment and a gateway to vibrant local culture. Whether you're at the forefront of the student experience, conducting world-changing research or supporting the engine that drives Texas’ flagship university, working at UT means making a lasting impact on our city, our state and our world.Our more than 20,000 faculty and staff empower 55,000+ students to challenge ideas, pursue passions and shape their futures. Joining UT, you’ll become part of a community dedicated to making a meaningful impact on campus and throughout the world.Please see our .**Comments and Inquiries:**Email comments to hrsc@austin.utexas.edu. For questions or concerns regarding equal opportunity only, contact .Additional information for applicants can be found on the Human Resources web page: .

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