Senior Data Engineer

Dell Medical School; Austin (TX); On-site; USD 117,000 - 159,000; Full time

Job summary

Dell Medical School seeks a Senior Data Engineer to lead design, development, and optimization of complex data pipelines and platforms for enterprise analytics across clinical, operational, financial, and research environments.

You will design and maintain ETL/ELT processes, integrate data from EHRs and other sources, ensure security and HIPAA compliance, and partner with IT and cloud teams to enable scalable data solutions.

Qualifications

• Bachelor’s degree in a related field.
• At least 6 years of experience in data engineering or data platform development.
• Demonstrated experience designing and leading complex data pipelines and platforms.
• Relevant education and experience may be substituted as appropriate.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines.
• Lead data integration from EHRs, financial systems, registries, and external sources.
• Optimize pipelines for performance, reliability, fault tolerance, and cost.
• Implement batch and near real-time processing as needed.
• Ensure HIPAA compliance and data privacy.
• Mentor Data Engineer staff and review designs and architecture artifacts.
• Partner with IT and cloud teams to support availability and disaster recovery.

Education

Bachelor's Degree in Computer Science, Data Engineering, Information Systems, or a related field

Master’s Degree in Data Science, Data Engineering, Computer Science, Informatics, or related field

Tools

SQL

Python

Spark

Airflow

AWS

GCP

Azure/Fabric

HL7

FHIR

HIPAA

Job description

The Senior Data Engineer at Dell Medical School will lead the design, development, and optimization of complex data pipelines and platforms that enable enterprise analytics, reporting, and advanced data use cases across clinical, operational, financial, and research environments.

Key Responsibilities

• Design, build, and maintain complex, scalable ETL/ELT pipelines for both structured and unstructured data.
• Lead data integration 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 processing patterns as needed.
• Ensure pipelines meet regulatory, privacy, and security requirements, including HIPAA.
• Serve as technical owner for assigned data platforms, domains, or subject areas (such as clinical analytics or operational reporting).
• Design and maintain data lake, warehouse, and data mart structures using cloud platforms.
• Develop and enforce data modeling standards, schema design practices, and partitioning strategies.
• Partner with IT and cloud teams to support 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 predictive analytics and AI/ML proof-of-concept and experimentation efforts.
• Improve data engineering workflows through automation, monitoring, and CI/CD for data pipelines.
• Refactor legacy pipelines and supporting 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.
• Mentor Data Engineer staff and review code, pipeline designs, and architecture artifacts for 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.
• 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.

Technologies

• SQL, Python, Spark
• Airflow
• AWS, GCP, Azure/Fabric
• HIPAA, HL7, FHIR

Requirements

• 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.

Preferred Certifications

• Cloud Certification
• Microsoft Certified: Azure Data Engineer Associate
• Google Cloud Professional Data Engineer
• AWS Certified Data Analytics – Specialty

Salary and Location

• Location: Austin, TX (onsite)
• Salary: USD 138,000 per year
• 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 contact information (at least one reference should be from a supervisor)
• Letter of interest

Certifications

• Required: None