Senior Cloud Data Engineer

University of Colorado•Remote, CO

1d•$89,926 - $114,386•Remote

About The Position

We are seeking a Senior Cloud Data Engineer to design, build, and scale our core data platform, including data warehouse and data lakehouse environments. This role is critical in establishing the foundation for reliable, secure, and high-performing data systems that enable analytics, applications, and strategic decision-making across the organization. This is a highly hands-on individual contributor role suited for someone who thrives in a fast-paced, evolving environment, is comfortable navigating ambiguity, and can take ownership of complex data challenges from design through implementation within a small, agile team.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (or equivalent practical experience).
A combination of education and related technical/military/paraprofessional experience may be substituted for a bachelor’s degree on a year-for-year basis.
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.
Advanced proficiency in SQL (Microsoft SQL Server and PostgreSQL) and Python for developing, optimizing, and maintaining cloud-based data pipelines, ETL/ELT workflows, and system integrations.
Knowledge of analytical data modeling principles and experience designing scalable data warehouse and lakehouse architectures.
Knowledge of cloud-native technologies and modern software engineering practices, including serverless computing, containerization, source control (Git), and CI/CD automation.
Knowledge of cloud security principles and best practices for protecting sensitive and regulated data, including PHI, PII, encryption, identity and access management, and data governance.
Ability to design, implement, and support secure, scalable, reliable, and maintainable cloud data solutions.
Ability to work effectively in Agile environments, manage multiple priorities, and adapt to changing business and technical requirements.
Strong written and verbal communication skills, with the ability to collaborate effectively with technical and non-technical stakeholders and build productive working relationships across all organizational levels.
Strong customer service orientation with a commitment to delivering high-quality solutions and responsive support.

Nice To Haves

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.

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.