About
Responsibilities Creates and maintains optimal data pipeline architecture for structured and unstructured healthcare data. Assembles large, complex data sets that meet functional and non‑functional business requirements. Builds scalable ETL/ELT pipelines using SQL and AWS big data technologies. Optimizes pipeline performance for latency, throughput, and fault tolerance. Ensures pipelines comply with HIPAA and other regulatory standards.
Builds infrastructure for optimal extraction, transformation, and loading of data from diverse sources. Creates and maintains data lakes, warehouses, and marts using platforms like Snowflake, Redshift, or BigQuery. Configures cloud‑based storage and compute environments (AWS, Azure, GCP). Implements schema design, indexing, and partitioning strategies. Ensures high availability and disaster recovery protocols.
Creates data tools for analytics and data science teams to build and optimize data products. Develops reusable components for reporting and dashboarding tools. Builds data models and views for use by analysts and data scientists. Enables self‑service analytics through curated datasets. Collaborates with stakeholders to define KPIs and metrics.
Identifies, designs, and implements internal process improvements. Automates manual processes and optimizes data delivery. Re‑designs infrastructure for greater scalability and performance. Refactors legacy systems for maintainability. Implements CI/CD pipelines for data workflows.
Works with stakeholders including Executive, Product, Data, and Design teams to support data infrastructure needs. Translates business requirements into technical specifications. Provides mentorship to junior data engineers. Communicates technical concepts to non‑technical stakeholders. Supports cross‑functional initiatives and agile squads.
Keeps data separate and secure, following all relevant governance and security protocols. Implements data validation, anomaly detection, and cleansing routines. Collaborates with data governance teams to enforce policies. Audits data for completeness, accuracy, and timeliness. Supports data stewardship and master data management initiatives.
Conducts training sessions for analysts and clinical staff on data tools. Participates in vendor evaluations and proof‑of‑concept projects. Supports data integration for mergers, acquisitions, or new service lines. Assists in disaster recovery drills and business continuity planning. Contributes to grant proposals or research initiatives requiring data support. Performs related duties as required.
Knowledge, Skills, and Abilities Quickly learns new technical skills and knowledge; adopts new data tools and frameworks with minimal supervision. Learn and apply healthcare‑specific data standards (e.g., HL7, FHIR). Keeps current with cloud platform updates and best practices.
Uses rigorous logic and methods to solve difficult problems with effective solutions. Diagnoses root causes of data pipeline failures. Designs scalable solutions for complex data integration challenges. Applies statistical methods to validate data quality.
Possesses the functional and technical knowledge and skills to do the job at a high level of accomplishment. Writes efficient SQL and Python code for data processing. Configures cloud infrastructure for data workloads. Implements secure and compliant data architectures.
Copes with change effectively; can shift gears comfortably; decides and acts without having the total picture. Designs flexible data models for evolving clinical needs. Navigates incomplete or inconsistent data sources. Adapts to shifting priorities in fast‑paced environments.
Works effectively with others to achieve shared goals; actively listens and communicates openly. Partners with clinicians to understand data needs. Participates in cross‑functional agile teams. Resolves conflicts between technical and business priorities.
Sees ahead clearly; anticipates future consequences and trends accurately. Designs data systems that scale with organizational growth. Aligns data engineering efforts with enterprise analytics strategy. Anticipates regulatory changes and prepares infrastructure accordingly.
Required Qualifications Bachelor’s Degree in Computer Science, Information Systems, Engineering, Statistics, or a related field with at least 2 years of experience in data engineering, architecture, or ETL development. Proficiency with big data tools (e.g., Hadoop, Spark, Kafka). Experience with both SQL and NoSQL databases. Skilled in data pipeline and workflow management tools. Familiarity with AWS services, such as EC2, EMR, RDS, Redshift, Glue, DynamoDB. Programming/scripting experience in Python, Java, C++, Scala, or similar. Relevant education and experience may be substituted as appropriate.
Preferred Qualifications Master’s Degree in Data Engineering, Computer Science, or related field with at least 5 years of experience in healthcare data engineering or analytics. Advanced SQL skills and hands‑on relational database work. Expertise in building and optimizing big data pipelines using Python. Experience managing data transformation, metadata, dependencies, and workflow orchestration. Understanding of message queuing, stream processing, and scalable data storage systems. Strong project management skills.
Salary Range $71,059.66 + depending on qualifications
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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Languages
- English
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