Senior Cloud Data Engineer

The University of Colorado Anschutz Medical Campus is seeking a Senior Cloud Data Engineer to design, build, and scale their core data platform. This role involves establishing reliable and high-performing data systems that enable analytics and strategic decision-making across the organization.

Responsibilities:

• Design, develop, and evolve cloud-based data warehouse and lakehouse architectures
• Architect and implement scalable data pipelines and integration frameworks, core data integrations, ETL/ELT pipelines using Python and SQL across a wide range of data sources, including: Ingesting transactional data from PostgreSQL and other relational systems
• Processing large-scale data exports & file-based ingestion (e.g., S3, Azure Blob, SFTP, etc.) and mastering data into unified analytical models
• Supporting interoperability through healthcare integrations using FHIR protocols
• Integrating specialized systems such as medical record curation platforms into centralized data environments
• RESTful APIs
• Application backends
• Design and deliver data products, curated datasets, and analytical data models, transforming normalized (3NF) source data into performant, analytics-ready structures (e.g., star/snowflake schemas)
• Optimize storage and processing strategies within lakehouse environments (e.g., Parquet, partitioning, efficient query design)
• Establish and follow best practices for data quality, reliability, observability, and performance
• Collaborate with stakeholders to define and deliver solutions in situations where requirements may be ambiguous, incomplete, or rapidly evolving
• Contribute to and maintain CI/CD pipelines and DevOps practices for data engineering, including automation and deployment strategies
• Participate in Agile workflows using Jira, contributing to backlog refinement, sprint planning, and delivery execution

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Engineering, or related field (or equivalent practical experience)
• 2 years' professional data engineering experience, including designing analytical data models, developing Python‑based ETL/ELT pipelines, optimizing SQL across MSSQL/PostgreSQL, integrating diverse data sources (APIs, relational, file‑based), leveraging cloud compute services (Azure Functions, AWS Lambda), and collaborating in Agile environments with Git‑based version control
• Applicants must meet minimum qualifications at the time of hire
• Applicants must be legally authorized to work in the United States without requiring sponsorship
• Experience developing cloud-based data engineering solutions using modern data platforms (e.g., Databricks, Snowflake, Azure Synapse)
• Experience designing scalable data pipelines, distributed data processing solutions, and data lake architectures using modern storage formats (e.g., Parquet)
• Experience implementing workflow orchestration, containerization, and CI/CD practices using tools such as Airflow, Prefect, Azure Data Factory, Docker, and GitHub Actions
• Experience building data products that support analytics, reporting, data science, or operational applications
• Experience working with healthcare data standards (e.g., FHIR, HL7) and regulated data environments
• Experience working in a higher education or academic environment
• Experience working with healthcare, clinical, or biomedical research data in a healthcare, academic medical center, or life sciences environment