Career openings for D2I will evolve as we post new positions and fill current openings.
See our complete roster of current openings and check back often.
Data Architect
The Data Architect works closely with the Chief Enterprise Data Architect to augment data architecture and management, increase capacity for complex projects, and improve overall alignment with business objectives. The Data Architect leverages their creativity to solve complex problems and build effective relationships through open communication.
- Design and implement robust and scalable cloud-based data solutions, to meet executive priorities.
- Develop and maintain a comprehensive data strategy that aligns with enterprise goals.
- Develop and implement standards and best practices related to data architecture, under the supervision and guidance of the Chief Enterprise Data Architect.
- Research and and recommend new technologies to enhance our data capabilities.
Data QA Engineer
The Data QA 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. The Data QA Engineer works closely with other team members to design and implement solutions focused on validation of data within an AWS-based cloud data platform. The ideal candidate will be responsible for ensuring accuracy, integrity, and reliability of data within dimensional data models. This role demands a detail-oriented and analytical individual who thrives in a fast-paced environment and is passionate about maintaining high data quality standards.
- Develop and execute comprehensive test plans to validate data within AWS-based cloud data warehouse.
- Conduct thorough testing of ETL processes, dimensional data models, and reporting outputs.
- Collaborate with the data engineering team to define quality benchmarks and metrics.
- Work closely with data architect, data modelers, engineers, and analysts to understand data requirements and deliverables.
Data ETL Engineer
The Data ETL 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. They develop extract, transform, and load (ETL) procedures using integration tools such as Informatica for complex data models that reflect academic and administrative business processes at the University of Texas at Austin. The Data ETL Engineer plays a critical part in establishing a valuable cloud data ecosystem warehouse that supports critical business decisions and data analysis processes for the campus community. The Data ETL Engineer leverages their creativity to solve complex data and business problems and builds effective relationships through open communication.
- Develop ETL programs for complex dimensional data models within modern data platforms such as AWS Aurora Postgres, Snowflake, etc.
- Adapt ETL programs to evolving data models and business needs
- Partner with key stakeholders including enterprise data architect, subject matter experts (SMEs), data modelers, and teammates
- Design and automate ETL solutions for data cleansing, preparation, data validation and related processes
Analytics Engineer
The Analytics Engineer operates at the intersection of data analytics, data engineering, and business intelligence to build robust, efficient, and scalable data structures to support high-quality, nuanced, and timely data analytics to empower strategic decisions and policies by University leadership and stakeholders.
- Develops, codes, validates, and implements relational databases and integrated data structures that support analytics.
- Collaborates with functional partners to collect business requirements and develop and refine business logic.
- Collaborates with technical partners to locate, clean, and orchestrate source data.
- Works both independently and collaboratively with cross-functional teams to develop exceptional data products to meet university needs.
Assistant Data Scientist
The Assistant Data Scientist develops and builds high-quality, nuanced, and timely data analytics to empower strategic decisions and policies by University leadership and stakeholders. This position supports strategic analytics projects for decision-makers within the Provost’s Office, the President’s Office, and other University leadership.
- Develops and maintains descriptive, predictive, and prescriptive analytics and consumable reports, dashboards, and other analytical tools to provide University leadership and various stakeholders with necessary data to support decision-making.
- Applies advanced statistical modeling, reusable and scalable coding, data visualization techniques, and other analytical skills to translate business logic and produce deliverables in response to executive leadership’s requests.
- Works both independently and collaboratively with cross-functional teams to develop exceptional data products to meet university needs.
Product Owner
As part of the Data to Insights (D2I) Initiative, the Product Owner will play a pivotal role in guiding and shaping the development of products that integrate and simplify university data for insightful decision-making. The successful candidate will collaborate with stakeholders from various university offices and work closely with cross-functional teams comprising functional, analytical, and technical experts. We are seeking an individual who can drive product development, uphold Agile principles, and contribute to the overall success of the initiative while embodying our core values of respect, focus, openness, commitment, and courage.
- Collaborate with stakeholders to define and prioritize the product backlog, ensuring alignment with the overall goals D2I.
- Work closely with development teams to ensure a shared understanding of product goals, features, and priorities.
- Lead product and release planning efforts and actively participate in Scrum ceremonies such as daily stand-ups, sprint planning, sprint demos, and retrospectives.
- Drive product backlog refinement, ensuring concise acceptance criteria that are well understood by team members, clear minimum viable product agreements, and effective prioritization.