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Data Engineer IIUniversity of Texas at Austin • Austin, Texas, United States
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Data Engineer II

University of Texas at Austin
  • US
    Austin, Texas, United States
  • US
    Austin, Texas, United States
Postuler Maintenant

À propos

Job Posting Title: Data Engineer II ---- Hiring Department: Dell Medical School ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 40 ---- FLSA Status: Exempt from FLSA ---- Earliest Start Date: Immediately ---- Position Duration: Expected to Continue ---- Location: AUSTIN, TX ---- Job Details: General Notes Data Engineer II is an experienced data professional responsible for designing, building, and maintaining robust data pipelines and infrastructure that enable the collection, storage, and processing of large datasets. This role expands upon the Data Engineer I position by handling more complex data projects and working with greater independence. A Data Engineer II ensures data is accurate, secure, and compliant with data governance standards. A Data Engineer II collaborates with cross-functional teams (e.g., business stakeholders, IT, and subject-matter experts) to deliver solutions that meet business and research needs. Responsibilities
Maintains and optimizes data pipeline architecture by designing, building, and managing ETL processes that extract, transform, and load data from diverse sources. Assembles large, complex data sets to meet both functional and non-functional requirements, and develops scalable architectures for structured and unstructured data.
Integrates and consolidates data from multiple systems-such as disparate databases and electronic health records-into unified repositories like data warehouses or data lakes. Develops and enhances the underlying data infrastructure using SQL and cloud technologies to ensure scalability and reliability.
Creates and supports analytics tools that empower analysts and data scientists to access and analyze data efficiently. Builds custom queries, scripts, and dashboards that enable insight generation and data product optimization. Collaborates with analytics experts to organize, query, and visualize data for reporting and research.
Identifies and implements process improvements to enhance data operations. Automates manual workflows, optimize data delivery pipelines, and redesign system architecture to support scalability and performance. Continuously evaluates workflows and technologies to recommend improvements that accommodate growing data complexity.
Ensures data governance and security by validating data for accuracy and consistency, and maintaining secure, compliant data environments. Follows best practices and regulatory standards (e.g., HIPAA) to protect sensitive information and uphold data integrity.
Collaborates with stakeholders across departments-including executives, product managers, researchers, and designers-to address data infrastructure needs and resolve technical issues. Translates non-technical requirements into effective data solutions and advises on best practices for data architecture.
Manages and executes data projects from planning through deployment. Applies light project management techniques to coordinate tasks, communicates with team members, and ensures timely delivery. Exercises independent judgment to overcome obstacles and align project outcomes with organizational goals.
MARGINAL OR PERIODIC FUNCTIONS:
Adheres to internal controls and reporting structure.
Performs related duties as required.
KNOWLEDGE/SKILLS/ABILITIES
Systems Knowledge: Broad understanding of system-level concepts in computing. This includes knowledge of programming and scripting, operating systems, database query languages (SQL) and data mining techniques, as well as familiarity with IT infrastructure (servers, networking, cloud services). Such knowledge enables the Data Engineer II to troubleshoot and optimize across the technology stack.
Big Data Processing: Proficiency with big data frameworks such as Apache Spark for distributed data processing and large-scale computations. Experience optimizing Spark jobs for performance is often required.
Workflow Orchestration: Experience with workflow orchestration tools like Apache Airflow (or similar platforms) to schedule and manage complex data pipelines. Ability to design reliable job workflows and handle dependencies between tasks.
Programming & Databases: Strong programming skills in Python (especially using PySpark) and solid knowledge of SQL for querying and manipulating data. Familiarity with working in both relational databases (SQL) and NoSQL databases, with the ability to design and optimize database schemas and queries for each.
Version Control: Experience using Git or other version control systems for managing codebases and collaborating on data projects. Follows best practices in code versioning and documentation to maintain a clear history of changes.
Cloud Data Pipelines: Hands-on experience building data pipelines on cloud or modern data platforms. This could include using services in Microsoft Fabric (e.g., Azure Data Factory within Fabric) or similar ETL tools to move and transform data at scale. Knowledge of cloud ecosystems and services for data processing (such as AWS Glue or Azure Synapse pipelines) is beneficial.
Data Warehousing: Familiarity with cloud-based data warehousing and analytics services such as Google BigQuery, Microsoft Fabric (Synapse Analytics), or AWS Redshift for storing and querying large datasets. Ability to optimize data models and SQL queries on these platforms to ensure fast performance and cost-efficiency.
Domain Expertise: (If applicable) Experience working with healthcare or clinical data is highly valuable. For example, familiarity with electronic health record (EHR) systems and clinical registries, experience using tools like REDCap for data capture, or involvement in healthcare analytics projects. Ability to create quality/outcome reports and develop data visualizations for non-technical stakeholders is a plus.
Technical Learning
Quickly grasps technical concepts and applies them effectively.
Learns new tools and platforms independently.
Applies new techniques to improve data pipelines.
Shares technical knowledge with peers.
Problem Solving
Uses logic and data to solve complex problems effectively.
Diagnoses root causes of data issues.
Designs scalable solutions.
Anticipates and mitigates risks.
Action Oriented
Takes initiative and acts with urgency
Proactively addresses data quality issues
Suggests improvements without being prompted
Delivers results under tight deadlines
Collaboration
Works effectively with others to achieve shared goals.
Communicates clearly with non-technical stakeholders.
Participates in cross-functional teams.
Resolves conflicts constructively.
Planning and Organizing
Prioritizes tasks and manages time effectively.
Breaks down complex projects into manageable steps.
Tracks progress and adjusts plans as needed.
Meets deadlines consistently.
Required Qualifications Requires a Bachelor's Degree in Computer Science, Information Systems, Data Science, or a related field (required). An equivalent combination of relevant education and experience may be considered in lieu of a four-year degree with at least 4 year(s) of experience in data engineering or a closely related field. This experience should include designing data architectures, developing data pipelines, and implementing data quality/performance monitoring. Proven track record in database development using Python and SQL (including experience with NoSQL databases) is expected. Preferred Qualifications Master's Degree in Computer Science, Data Engineering, Informatics, or a related field with at least 7 year(s) of experience in healthcare data engineering or enterprise data systems. LICENSES, REGISTRATIONS OR CERTIFICATIONS REQUIRED:
N/A
PREFERRED:
Microsoft Certified: Azure Data Engineer Associate
Google Cloud Professional Data Engineer
AWS Certified Data Analytics - Specialty
Analytical Skills: Knowledge of statistics and experience with statistical or data analysis software or Python libraries for data science. This background helps in understanding data trends and supporting data scientists or analysts in the organization with more advanced analytics needs.
Salary Range $89,946 + depending on qualifications Working Conditions
Works in a typical office setting with standard equipment (computer, phone, etc.)
May work remotely or in a hybrid environment depending on organizational policy.
Prolonged periods of sitting and working at a computer.
May require occasional travel between healthcare system locations.
For healthcare workers: May be required to enter clinical environments for data integration or support.
Required Materials
Resume/CV
3 work references with their contact information; at least one reference should be from a supervisor
Letter of interest
Importantfor 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 thatALLRequired Materials have been uploaded. Once your job application has been submitted, you cannot make changes. Important 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 questionspresented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above. ---- Employment Eligibility: Regular 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. ---- Retirement Plan Eligibility: The 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 days in length. ---- Background Checks: A criminal history background check will be required for finalist(s) under consideration for this position. ---- Equal Opportunity Employer: The University of Texas at Austin, as an equal opportunity/affirmative action employer,complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions. ---- Pay Transparency: The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information. ---- Employment Eligibility Verification: If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university. ---- E-Verify: The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university's company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:
E-Verify Poster (English and Spanish) [PDF] Right to Work Poster (English) [PDF] Right to Work Poster (Spanish) [PDF]
---- Compliance: Employees m
Required
Preferred
Job Industries
Other
  • Austin, Texas, United States

Compétences linguistiques

  • English
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