Senior Data Engineer

Job Title: Senior Data Engineer Hiring Department: Dell Medical School Location: Austin, TX Weekly Hours: 40 Start Date: Immediately Position Duration: Expected to Continue Job Overview The Senior Data Engineer leads the design, development, and optimization of complex data pipelines and platforms that support enterprise analytics, reporting, and advanced data use cases. Acting as a technical leader, the role owns moderate‑to‑large initiatives, guides architectural decisions, and mentors junior engineers, while partnering with data scientists, analysts, software engineers, informaticists, and business stakeholders to deliver scalable, secure, and high‑quality data solutions across clinical, operational, financial, and research domains. Responsibilities Design, build, and maintain complex, scalable ETL/ELT pipelines for structured and unstructured data. Integrate data from EHRs, financial systems, registries, and external sources. Optimize pipelines for performance, reliability, fault tolerance, and cost efficiency. Implement batch and near‑real‑time processing patterns as needed. Ensure pipelines meet regulatory, privacy, and security requirements (e.g., HIPAA). Act as technical owner for specific data platforms or subject areas. Design and maintain data lake, warehouse, and data mart structures using cloud platforms. Develop and enforce data modeling standards, schema design, and partitioning strategies. Collaborate with IT and cloud teams to ensure availability, scalability, and disaster recovery readiness. Build curated, analytics‑ready datasets and reusable data assets for analysts and data scientists. Support feature engineering, model training, and deployment workflows for data science teams. Develop frameworks and patterns that improve self‑service analytics and reduce ad hoc data requests. Drive process improvement initiatives such as automation, monitoring, and CI/CD for data pipelines. Refactor legacy pipelines and infrastructure to improve maintainability and scalability. Establish best practices for code quality, documentation, testing, and version control. Evaluate new tools and technologies and recommend adoption where appropriate. Provide technical mentorship to Data Engineer staff, reviewing code, designs, and architecture artifacts. Contribute to onboarding, internal training, and knowledge‑sharing activities. Collaborate with business, clinical, research, and operational stakeholders to translate requirements into technical solutions. Lead data engineering workstreams within cross‑functional projects or agile squads. Communicate technical concepts, trade‑offs, and risks to non‑technical audiences. Support planning, estimation, and prioritization of data engineering initiatives. Support data integration for new service lines, acquisitions, or system migrations. Participate in vendor evaluations and technical assessments. Assist with disaster recovery testing and business continuity planning. Contribute to grant proposals or research initiatives requiring advanced data infrastructure. Knowledge, Skills & Abilities Technical Expertise: Advanced proficiency in SQL, Python, and related data engineering languages. Strong experience with distributed data processing frameworks such as Spark. Hands‑on expertise with workflow orchestration tools such as Airflow. Deep familiarity with cloud‑based data platforms and services (AWS, GCP, or Azure). Experience designing and optimizing data models for analytics and reporting. Data Governance & Compliance: Strong understanding of data governance, data quality, and security best practices. Experience working with regulated data, particularly healthcare or clinical data. Familiarity with healthcare data standards (HL7, FHIR) preferred. Problem Solving & Decision Making: Analyze complex systems to identify root causes and scalable solutions. Balance short‑term delivery with long‑term architectural sustainability. Makes sound technical decisions with limited ambiguity. Collaboration & Leadership: Collaborate effectively across technical and non‑technical teams. Provide constructive feedback and technical guidance to peers. Demonstrate ownership, accountability, and initiative. Required Qualifications Bachelor’s Degree in Computer Science, Data Engineering, Information Systems, or a related field. At least 6 years of experience in data engineering, analytics engineering, or data platform development. Demonstrated experience designing and leading complex data pipelines and data platforms. Relevant education and experience may be substituted as appropriate. Preferred Qualifications Master’s Degree in Data Science, Data Engineering, Computer Science, Informatics, or related field. Experience in healthcare data engineering or regulated data environments. Exposure to AI/ML infrastructure, feature stores, or model operationalization. Experience leading technical initiatives or acting as a team lead. Licenses, Registrations & Certifications None required. Preferred: Cloud Certification Microsoft Certified: Azure Data Engineer Associate Google Cloud Professional Data Engineer AWS Certified Data Analytics – Specialty Salary Range $138,000+ depending on qualifications 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. We do not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity or expression, disability, religion, or veteran status in employment, educational programs, and activities, and admissions. #J-18808-Ljbffr