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Senior Data EngineerPhase2 Technology • Austin, Texas, United States
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Senior Data Engineer

Phase2 Technology
  • US
    Austin, Texas, United States
  • US
    Austin, Texas, United States
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Job Posting Title:
Senior Data Engineer
Job Details The Senior Data Engineer is a highly experienced data professional responsible for leading the design, development, and optimization of complex data pipelines and platforms that support enterprise analytics, reporting, and advanced data use cases. This role serves as a technical leader within the data engineering team, owning moderately large initiatives, guiding architectural decisions, and mentoring Data Engineers.
Reporting to the Director of Data Intelligence and Decision Science (or a designated senior leader), the Senior Data Engineer partners closely with data scientists, analysts, software engineers, informaticists, and business stakeholders. The role ensures scalable, secure, and high-quality data solutions while supporting organizational priorities in clinical, operational, financial, and research domains. The Senior Data Engineer plays a key role in preparing the organization for advanced analytics, automation, and AI/ML adoption, without holding full enterprise-wide ownership reserved for the Principal Data Engineer.
Responsibilities
Design, build, and maintain complex, scalable ETL/ELT pipelines for structured and unstructured data.
Lead integration of data from EHRs, financial systems, registries, and external data sources.
Optimize pipelines for performance, reliability, fault tolerance, and cost efficiency.
Implement batch and near-real-time data processing patterns as needed.
Ensure pipelines meet regulatory, privacy, and security requirements (e.g., HIPAA).
Serve as technical owner for specific data platforms, domains, or subject areas (e.g., clinical analytics, operational reporting).
Design and maintain data lake, warehouse, and data mart structures using cloud platforms.
Develop and enforce data modeling standards, schema design, and partitioning strategies.
Partner 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.
Collaborate with data science teams to support feature engineering, model training, and deployment workflows.
Develop frameworks and patterns that improve self-service analytics and reduce ad‑hoc data requests.
Support experimentation and proof‑of‑concept work for predictive analytics and AI/ML use cases.
Lead initiatives to improve data engineering workflows, including 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.
Serve as a technical mentor to Data Engineer staff.
Review code, pipeline designs, and architecture artifacts to ensure quality and consistency.
Provide guidance on complex technical problems and help unblock team members.
Contribute to onboarding, internal training, and knowledge‑sharing activities.
Partner 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.
MARGINAL OR PERIODIC FUNCTIONS
Support data integration efforts 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.
Perform related duties as required.
KNOWLEDGE / SKILLS / ABILITIES Technical Expertise
Advanced proficiency in SQL and Python and related languages for data engineering.
Strong experience with distributed data processing frameworks (e.g., Spark).
Hands‑on expertise with workflow orchestration tools (e.g., Airflow).
Deep familiarity with cloud‑based data platforms and services (AWS, GCP, or Azure/Fabric).
Experience designing and optimizing data models for analytics and reporting.
Data Governance and 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 (e.g., HL7, FHIR) preferred.
Problem Solving and Decision Making
Analyzes complex systems to identify root causes and scalable solutions.
Balances short‑term delivery with long‑term architectural sustainability.
Makes sound technical decisions with limited ambiguity.
Collaboration and Leadership
Effectively collaborates across technical and non‑technical teams.
Provides constructive feedback and technical guidance to peers.
Demonstrates 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 OR CERTIFICATIONS REQUIRED: None
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
Working Conditions
Standard office equipment
Repetitive use of a keyboard
May be exposed to healthcare‑related occupational hazards depending on assignment
Required Materials
Resume/CV
3 work references with their contact information; at least one reference should be from a supervisor
Letter of interest
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.
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  • Austin, Texas, United States

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  • English
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