{"id":163785,"url":"https://alion.io/job/utexas-lead-data-engineer","title":"Lead Data Engineer","company":{"id":3658,"name":"The University of Texas at Austin","domain":"utexas.edu","url":"https://alion.io/company/utexas","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":77,"open_postings":141,"ghost_share":0,"stale_share":0.901,"repost_share":0.007,"time_to_fill_p50_days":28,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":104000,"max_usd":179000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":98},"experience_years_min":8,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Airflow","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Flink","optional":true},{"name":"Machine Learning","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2025-12-05T00:00:00Z","employer_posted_date":"2025-12-05","last_verified_at":"2026-10-17T05:53:39Z","board_verified":true,"closed_at":null,"days_open":316,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":316},"description":"Job Posting Title:\nLead Data Engineer----\nHiring Department:\nEnterprise Technology - Data to Insights (D2I)----\nPosition Open To:\nAll Applicants----\nWeekly Scheduled Hours:\n40----\nFLSA Status:\nExempt----\nEarliest Start Date:\nImmediately----\nPosition Duration:\nExpected to Continue Until Dec 19, 2026----\nLocation:\nTexas----\nJob Details:\nGeneral Notes\nThis is a fixed term position that is expected to continue for a 1-year limited term from start date with a possibility for extension.\nFlexible work arrangements are available for this position, including the ability to work 100% remotely. Remote work for individuals who reside outside Texas but within the United States and its territories will be considered and requires Central Office approval.\nThis position provides life/work balance with typically a 40-hour work week and travel limited to training (e.g., conferences/courses).\nEnterprise Technology is dedicated to supporting the mission of the University of Texas at Austin of unlocking potential and preparing future leaders of the state.\nYour skills will make a difference.\nYou’ll be working for a university that is internationally recognized for research and the work you do will make a difference in the lives of our students, faculty and staff. If you’re the type of person that wants to know your work has meaning and impact, you’ll like working for our campus.\nThe University of Texas at Austin and Enterprise Technology provide an outstanding benefits package to our staff. Those benefits include:\nCompetitive health benefits (Employee premiums covered at 100%; family premiums at 50%)\n\nVision, dental, life, and disability insurance options\n\nPaid vacation, sick leave, and holidays\n\nTeachers Retirement System of Texas (a defined benefit retirement plan)\n\nAdditional voluntary retirement programs: tax sheltered annuity 403(b) and a deferred compensation program 457(b)\n\nFlexible spending account options for medical and childcare expenses\n\nTraining and conference opportunities\n\nTuition assistance\n\nAthletic ticket discounts\n\nAccess to UT Austin's libraries and museums\n\nFree rides on all UT Shuttle and Capital Metro buses with staff ID card\n\nFor more details, please see: https://hr.utexas.edu/prospective/benefits and https://hr.utexas.edu/current/services/my-total-rewards\nMust be authorized to work in the United States on a full-time basis for any employer without sponsorship.\nThis position requires you to maintain internet service and a mobile phone with voice and data plans to be used when required for work.\nPurpose\nThe Lead Data 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 useability and value of institutional data. You will lead senior data engineers and data engineers to create complex data pipelines within UT’s cloud data ecosystem in support of academic and administrative needs. In collaboration with our team of data professionals, you will help build and run a modern data hub to enable advanced data-driven decision making for UT. You will leverage your creativity to solve complex technical problems and build effective relationships through open communication within the team and outside partners.\nResponsibilities\nTechnical Leadership:\nDesign, architect, and deliver production-grade, scalable data pipelines and AI-ready data platforms using Databricks, AWS cloud-native services and modern data engineering frameworks. \nLead end-to-end implementation of lakehouse data pipelines, ensuring performance, reliability, and cost efficiency. \nChampion industry best practices for data engineering. \nConduct and participate in peer code reviews to maintain code quality and consistency across the team. \nProactively identify and resolve bottlenecks in data ingestion, transformation, and orchestration processes using Databricks Delta Live Tables, Spark optimization techniques, and workflow automation. \nImplement systems for data quality, observability, governance, and compliance using tools such as Unity Catalog, Delta Lake, and data validation frameworks. \nLead technical knowledge-sharing sessions on topics such as AI/ML integration, data lakehouse architecture, and emerging data technologies. \nProject Management:\nDefine project milestones, timelines, and deliverables for data and AI initiatives, ensuring timely and high-quality outcomes. \nCollaborate with both internal and external stakeholders such as data architects, system architects, business users, Agile team members, and other D2I internal groups. \nManage project priorities, sprint planning, and team workloads while balancing innovation with delivery. \nCommunicate risks, dependencies, and resource constraints effectively, and develop mitigation plans for on-time project delivery. \nTeam Management and Leadership:\nSupervise and mentor a team of Data Engineers (2-5 individuals) working on cloud, Databricks, and AI pipeline initiatives. \nFoster a culture of continuous learning, experimentation, and technical excellence, encouraging engineers to explore AI and automation use cases. \nParticipate in recruiting, onboarding, and developing data engineering talent with strong Databricks and AI skillsets. \nConduct performance reviews, set development goals, and create individualized growth plans for team members. \nEncourage collaboration across Data, AI/ML, Analytics, and Infrastructure teams to drive cross-functional success. \nCommunication:\nProvide regular updates on project progress, technical challenges, and project milestones to both technical and business stakeholders. \nTranslate complex technical concepts related to Databricks, AI, and data architecture into clear narratives for non-technical audiences. \nFoster a transparent communication culture and provide actionable feedback to promote a growth mindset. \nEnsure all data engineering processes, architectures, and standards are well-documented for reuse, governance, and knowledge continuity. \nInnovation and Other Duties:\nStay current with advancements in AI, data engineering, and Databricks ecosystem, evaluating new tools and frameworks for potential adoption. \nPilot and promote innovative solutions such as AI-assisted data quality checks, data observability automation, and intelligent pipeline optimization. \nPerform other duties as assigned, contributing to the organization’s data-driven and AI-enabled transformation. \nRequired Qualifications\nBachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience. \n5+ years of experience designing, implementing, and optimizing complex, production-grade data pipelines or enterprise-scale data platforms. \n5 years of experience in cloud-based data engineering using Databricks and Amazon Web Services (AWS) (e.g., Glue, S3, Lambda, Redshift). \n3+ years of experience managing or leading teams of data and/or software engineers, including mentorship, performance management, and project delivery. \nExpertise in Python, PySpark, and SQL, with strong understanding of data modeling, stored procedures, and scalable data transformations. \nProven experience architecting and implementing ETL/ELT solutions across relational, non-relational, and lakehouse environments (e.g., Delta Lake, Parquet, or Iceberg). \nExperience designing and managing CI/CD pipelines and infrastructure as code (IaC) using tools such as Databricks Repos, CDK, Terraform, or GitHub Actions. \nDemonstrated knowledge of test-driven development (TDD) and data quality frameworks, ensuring reliability and reproducibility across data workflows. \nDeep understanding of data governance, security, and compliance standards in cloud environments. \nExcellent analytical, problem-solving, and debugging skills across distributed data systems. \nProven ability to communicate complex technical concepts clearly to both technical and non-technical audiences. \nExperience supervising, mentoring, and guiding junior team members on technical and professional development. \nEquivalent combination of relevant education and experience may be substituted as appropriate.\nPreferred Qualifications\n8+ years of experience in Data Engineering or related fields, including 5+ years of hands-on experience building and optimizing data pipelines on Databricks or similar large-scale data platforms. \nProven experience implementing lakehouse architectures leveraging Databricks Delta Lake, Delta Live Tables, and Unity Catalog for governance and scalability. \nExperience designing AI-ready data platforms and integrating machine learning pipelines using tools such as MLflow or model registry frameworks. \n3+ years of experience managing or leading cross-functional technical teams, fostering collaboration between Data Engineering, Analytics, and AI/ML teams. \n5+ years of experience with Agile software development methodologies and project tracking systems such as JIRA. \nExpertise in distributed data processing and streaming frameworks, such as Apache Spark, Kafka, Flink, or Airflow, for orchestration and automation. \nStrong familiarity with data observability, cost optimization, and performance tuning in Databricks and cloud-native architecture. \nProfessional certifications such as Databricks Certified Data Engineer Professional or AWS Solutions Architect or AWS Data Analytics Specialty are highly desirable. \nDemonstrated ability to introduce new technologies and best practices to modernize existing data environments and promote AI/analytics maturity across the organization. \nPassion for continuous learning and staying current with emerging technologies in data engineering, AI integration, and Databricks ecosystem advancements. \nSalary Range\n$125,000 - $143,712\nWorking Conditions\nMay work around standard office conditions\nRepetitive use of a keyboard at a workstation\nUse of manual dexterity (ex: using a mouse)\nWork Shift\nMonday - Friday 8am-5pm; Occasional nights or weekends may be required\nRequired Materials\nResume/CV\n3 work references with their contact information; at least one reference should be from a supervisor\nLetter of interest\nImportant for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.\nImportant for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.\n----\nEmployment Eligibility:\nRegular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.----\nRetirement Plan Eligibility:\nThe retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 day...","description_format":"text","description_chars":14996,"description_truncated":true,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Conference opportunities","Continuous learning","Flexible schedule","Professional development","Retirement plans"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Education","Science & Engineering","Higher Education","STEM Education"],"lifecycle":[{"event":"open","at":"2025-12-05T00:00:00Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 159 · green card filings: 17","filings_12m":159,"filings_prev_12m":199,"green_card_filings_12m":17,"median_offered_wage_usd":93466,"route":null,"cap_exempt":true,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":0}],"liveness":{"score":4,"band":"cold","label":"Long shot","p_open":1,"p_active":0.135,"p_room":0.28,"age_days":316,"expected_fill_days":28,"reasons":["conf:4","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/utexas-lead-data-engineer","json_url":"https://alion.io/job/utexas-lead-data-engineer.json","meta":{"generated_at":"2026-10-17T22:42:50Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":795,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3658},"rest":"https://alion.io/mcp/rest/get_company?id=3658"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Futexas-lead-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Futexas-lead-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Futexas-lead-data-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/utexas-lead-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Futexas-lead-data-engineer"}]}