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Development. Required Qualifications Bachelor’s Degree in computer science, data science, electrical engineering, or any other science/engineering field with a focus on AI, computation, large language models
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), 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
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protocol. This position will focus on the evaluation of current methodologies to improve processes that gather, prepare, transform, maintain, analyze, interpret, and report on large amounts of data generated
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messengers for the institution, Proactively meet with leaders and key faculty members, seizing opportunities to connect them with reporters and position them for interview success. Data Analysis, Tracking and
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. Responsibilities Develop and implement innovative statistical and computational approaches for the analysis of large datasets. These datasets may utilize several types of available data sources, including public
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related fields, including 5+ years of hands-on experience building and optimizing data pipelines on Databricks or similar large-scale data platforms. Proven experience implementing lakehouse architectures
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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
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known for its leadership in high performance computing, visualization, big data analytics, and machine learning. UT Austin hosts a broad range of Centers and interdisciplinary initiative, including
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data supporting institutional decision-making, data requests, and federal and state reporting. This role provides an excellent opportunity to work with large datasets, develop coding skills and
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campus personnel, such as professionals in academic affairs and other student services providers. Ability to work within a large system, and to participate and collaborate effectively in a large mental