The University of Texas at Austin
Senior Data Engineer
– The University of Texas at Austin
Job Summary
Enterprise Technology – Data to Insights (D2I) department
Position type: Fixed‑term, 1 year with possibility for extension
Weekly scheduled hours: 40
Location: Texas (remote work available for Texas residents; remote for non‑Texans requires Central Office approval)
Start date: Immediately
Responsibilities
Lead design, development, and automation of scalable, high‑performance data pipelines across institutional systems, AWS, Databricks, and vendor APIs.
Implement Databricks Lakehouse architectures for AI‑ready data platforms.
Create robust ETL/ELT workflows using Databricks, Spark, Delta Lake, and Python.
Ensure performance, reliability, and data quality through monitoring, optimization, and alerts.
Partner with stakeholders to define pipeline parameters and align with analytics and AI goals.
Adhere to university security, compliance, and governance guidelines.
Maintain technical documentation of pipeline designs and operations.
Collaborate with data architects, modelers, stewards, and experts to ensure consistency across the ecosystem.
Evaluate emerging technologies such as Unity Catalog, MLflow, Delta Live Tables, and AI‑driven observability tools.
Modernize pipelines for AI‑readiness and future integration with ML models.
Stay current with Databricks and cloud technology advances and advocate responsible adoption.
Promote knowledge sharing, mentor junior engineers, and participate in change management processes.
Required Qualifications
Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent experience.
At least 2 years of cloud‑based data engineering experience (AWS, Azure, or GCP) with emphasis on Databricks or Spark.
Proven experience building scalable, production‑grade pipelines across multiple systems.
Proficiency in Python and SQL for efficient, reusable code.
Strong knowledge of ETL/ELT principles, lakehouse architectures, and data quality monitoring.
Experience with CI/CD pipelines for data workflows (GitHub Actions, Azure DevOps, Jenkins).
Familiarity with data governance, security, and compliance in cloud environments.
Analytical, troubleshooting, and performance optimization skills for large‑scale distributed data systems.
Excellent communication and collaboration skills.
Experience mentoring peers on technical projects.
Preferred Qualifications
5+ years of data engineering or related technical leadership experience.
3+ years developing and optimizing Databricks pipelines (Delta Lake, Delta Live Tables, Workflows).
Experience designing AI‑ready architectures and integrating with ML/analytics environments.
Experience with Spark, Kafka, or Flink.
Databricks or AWS certifications (e.g., Databricks Certified Data Engineer Pro, AWS Solutions Architect).
Agile development experience with JIRA, Confluence, or similar tools.
Hands‑on experience with orchestration tools (Airflow, Databricks Workflows, AWS Step Functions).
Exposure to MLflow or model monitoring in MLOps.
Experience leading or supervising small teams or project‑based technical efforts.
Passion for continuous learning and staying current with Databricks, cloud data engineering, and AI enablement.
Salary Range $115,000 – $124,968
Working Conditions
Standard office conditions
Keyboard and mouse use required
Manual dexterity for computer work
Work Shift Monday – Friday 8 am – 5 pm; occasional nights or weekends may be required.
Required Materials
Resume/CV
Three work references; at least one from a supervisor
Letter of interest
Equal Opportunity Employer The University of Texas at Austin is an equal‑opportunity/affirmative action employer and complies with all applicable federal and state laws regarding nondiscrimination and affirmative action.
Pay Transparency The University of Texas at Austin will not discriminate based on inquiries about pay or pay disclosure. Employees who have access to compensation information are not permitted to disclose pay to non‑eligible individuals.
Employment Eligibility Verification Hired employees must complete the I‑9 form and provide acceptable documents proving identity and authorization to work in the United States within three days of employment.
E‑Verify The University of Texas at Austin uses E‑Verify for all new hires. The company ID number is 854197.
Compliance Employees may be required to report violations of law under Title IX and the Clery Act. All prospective employees are notified of the Annual Security and Fire Safety report.
#J-18808-Ljbffr
– The University of Texas at Austin
Job Summary
Enterprise Technology – Data to Insights (D2I) department
Position type: Fixed‑term, 1 year with possibility for extension
Weekly scheduled hours: 40
Location: Texas (remote work available for Texas residents; remote for non‑Texans requires Central Office approval)
Start date: Immediately
Responsibilities
Lead design, development, and automation of scalable, high‑performance data pipelines across institutional systems, AWS, Databricks, and vendor APIs.
Implement Databricks Lakehouse architectures for AI‑ready data platforms.
Create robust ETL/ELT workflows using Databricks, Spark, Delta Lake, and Python.
Ensure performance, reliability, and data quality through monitoring, optimization, and alerts.
Partner with stakeholders to define pipeline parameters and align with analytics and AI goals.
Adhere to university security, compliance, and governance guidelines.
Maintain technical documentation of pipeline designs and operations.
Collaborate with data architects, modelers, stewards, and experts to ensure consistency across the ecosystem.
Evaluate emerging technologies such as Unity Catalog, MLflow, Delta Live Tables, and AI‑driven observability tools.
Modernize pipelines for AI‑readiness and future integration with ML models.
Stay current with Databricks and cloud technology advances and advocate responsible adoption.
Promote knowledge sharing, mentor junior engineers, and participate in change management processes.
Required Qualifications
Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent experience.
At least 2 years of cloud‑based data engineering experience (AWS, Azure, or GCP) with emphasis on Databricks or Spark.
Proven experience building scalable, production‑grade pipelines across multiple systems.
Proficiency in Python and SQL for efficient, reusable code.
Strong knowledge of ETL/ELT principles, lakehouse architectures, and data quality monitoring.
Experience with CI/CD pipelines for data workflows (GitHub Actions, Azure DevOps, Jenkins).
Familiarity with data governance, security, and compliance in cloud environments.
Analytical, troubleshooting, and performance optimization skills for large‑scale distributed data systems.
Excellent communication and collaboration skills.
Experience mentoring peers on technical projects.
Preferred Qualifications
5+ years of data engineering or related technical leadership experience.
3+ years developing and optimizing Databricks pipelines (Delta Lake, Delta Live Tables, Workflows).
Experience designing AI‑ready architectures and integrating with ML/analytics environments.
Experience with Spark, Kafka, or Flink.
Databricks or AWS certifications (e.g., Databricks Certified Data Engineer Pro, AWS Solutions Architect).
Agile development experience with JIRA, Confluence, or similar tools.
Hands‑on experience with orchestration tools (Airflow, Databricks Workflows, AWS Step Functions).
Exposure to MLflow or model monitoring in MLOps.
Experience leading or supervising small teams or project‑based technical efforts.
Passion for continuous learning and staying current with Databricks, cloud data engineering, and AI enablement.
Salary Range $115,000 – $124,968
Working Conditions
Standard office conditions
Keyboard and mouse use required
Manual dexterity for computer work
Work Shift Monday – Friday 8 am – 5 pm; occasional nights or weekends may be required.
Required Materials
Resume/CV
Three work references; at least one from a supervisor
Letter of interest
Equal Opportunity Employer The University of Texas at Austin is an equal‑opportunity/affirmative action employer and complies with all applicable federal and state laws regarding nondiscrimination and affirmative action.
Pay Transparency The University of Texas at Austin will not discriminate based on inquiries about pay or pay disclosure. Employees who have access to compensation information are not permitted to disclose pay to non‑eligible individuals.
Employment Eligibility Verification Hired employees must complete the I‑9 form and provide acceptable documents proving identity and authorization to work in the United States within three days of employment.
E‑Verify The University of Texas at Austin uses E‑Verify for all new hires. The company ID number is 854197.
Compliance Employees may be required to report violations of law under Title IX and the Clery Act. All prospective employees are notified of the Annual Security and Fire Safety report.
#J-18808-Ljbffr