Senior 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
* Work both independently and collaboratively within cross-functional teams to deliver data products and pipelines that meet the University’s evolving data and analytics needs.
* Communicate clearly and effectively with technical and non-technical stakeholders regarding project progress, risks, dependencies, and technical challenges.
* Promote collaboration and knowledge sharing within the Data Engineering team through brainstorming sessions, design reviews, and Databricks best-practice discussions.
* Foster a culture of learning and innovation, supporting team morale and professional growth.
* Provide mentorship and peer guidance to junior data engineers on data pipeline design, Databricks workflows, and coding best practices.
* Participate in change management processes to ensure transparency and coordination across teams during system enhancements or platform migrations.
* Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
* At least two years of hands-on experience in Data Engineering using cloud-based platforms (AWS, Azure, or GCP) with emphasis on Databricks or Spark-based pipelines.
* Proven experience in designing, building, and automating scalable, production-grade data pipelines and integrations across multiple systems and APIs.
* Proficiency in Python and SQL, with demonstrated ability to write efficient, reusable, and maintainable code for data transformations and automation.
* Strong knowledge of ETL/ELT principles, data lakehouse architectures, and data quality monitoring.
* Experience implementing and maintaining CI/CD pipelines for data workflows using modern Dev Ops tools (e.g., Git Hub Actions, Azure Dev Ops, Jenkins).
* Familiarity with data governance, security, and compliance practices within cloud environments.
* Strong analytical, troubleshooting, and performance optimization skills for large-scale distributed data systems.
* Excellent communication and collaboration skills to work effectively with technical and non-technical stakeholders.
* Demonstrated experience mentoring and guiding junior engineers or peers on technical projects.
* Five or more years of experience in Data Engineering or related fields with increasing technical leadership responsibilities.
* Three or more years of experience developing and optimizing data pipelines on Databricks, including Delta Lake, Delta Live Tables, and Databricks Workflows.
* Experience designing AI-ready data architectures and integrating data workflows with machine learning and analytics environments.
* Experience with distributed data processing frameworks such as Spark, Kafka, or Flink.
* Databricks or AWS certifications (e.g., Databricks Certified Data Engineer Professional, AWS Solutions Architect, or AWS Data Analytics Specialty).
* Two or more years of experience in Agile software development environments, including use of tools such as JIRA, Confluence, or similar for issue tracking and project management.
* Hands-on experience with data orchestration tools (e.g., Airflow, Databricks Workflows, or AWS Step Functions).
* Exposure to data governance frameworks and AI/ML operations (MLOps) concepts such as MLflow or model monitoring.
* Demonstrated ability to lead or supervise small teams or project-based technical efforts.
* Passion for continuous learning and staying current with advancements in Databricks, cloud-based data engineering, and AI enablement.
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