Data Engineer III

Job Summary

Data Engineer III
Join the Utah Data Coordinating Center (DCC) as a Data Engineer, where your
work will directly enable innovative clinical research at the University of Utah and across national partners. You’ll lead the design of
scalable data systems, define and enforce architecture standards, and work alongside software developers, data analysts, and research teams
to ensure our platforms evolve with the needs of scientific discovery. This is a growth-focused role ideal for someone who thrives in a
collaborative, mission-driven environment. The Utah DCC supports large-scale health data infrastructure that
underpins national emergency response, clinical registries, and federal research initiatives.

Establish project teams and provide
overall direction for technical projects from initiation through to delivery. Perform project requirements, estimation, and budget
management. Formulate project scope and delivery strategies and establish milestones/schedules. Maintain and report project status and
monitor progress of all team members. Gather required data from end-users to evaluate objectives, goals, and scope to create technical
specifications. Serve as liaison between technical and non-technical departments in order to ensure that all targets and requirements are
met. Keep leadership informed of key issues that may impact project completion, budget, or other results.

The Utah DCC offers a career ladder for Data Engineers and provides growth and professional development
opportunities.

This position is not eligible for work visa sponsorship.

To learn more about the Utah DCC visit http://uofuhealth.org/UtahDCC

Job Responsibilities or Essential Functions:
As a Data Engineer, your responsibilities will
include:

1. Design, develop, and maintain database architecture following industry best practices
Design and
implement scalable, secure, and high-performing database solutions aligned with industry standards and architectural best practices. This
includes data modeling (conceptual, logical, and physical), schema design, indexing strategies, performance tuning, backup and recovery
planning, and ensuring data integrity and consistency. Establish governance standards, naming conventions, version control processes, and
documentation to support maintainability, reliability, and long-term scalability across environments.

2. Build, optimize, and
maintain scalable data pipelines
Design, develop, and orchestrate reliable, high-performance data pipelines from initial data
ingestion through final delivery. This includes data pipeline development, orchestration, transformation logic, and supporting data models
optimized for analytics and operational workloads.

3. Develop and optimize data processing and automation code

Design, implement, and maintain robust code for data extraction, transformation, integration, and analysis using appropriate
languages and frameworks. Optimize performance, ensure data accuracy, and uphold high standards for code quality, reliability, and
maintainability in alignment with software and data engineering best practices.

4. Drive continuous improvement and
innovation in cloud data technologies (AWS-focused)
Stay current with emerging data engineering
technologies, industry trends, and evolving AWS services to continuously enhance platform capabilities and
architectural standards. Evaluate and adopt appropriate AWS services (e.g., S3, Glue, Lambda, Redshift, RDS, EMR, Step Functions, Lake Formation) to improve scalability, performance, cost
efficiency, and reliability. Balance innovation with operational excellence by maintaining and optimizing existing services, enforcing best
practices, and ensuring stable, secure, and high-performing production environments.

5. Collaborate with business partners to
develop scalable data solutions
Partner with internal teams and external stakeholders to design and deliver innovative data
solutions that support evolving business needs. This includes developing and exposing data through APIs, building and maintaining
multi-dimensional cubes and semantic models, enabling secure data sharing, and creating reusable data services. Translate business
requirements into scalable technical solutions that align with enterprise architecture standards, governance policies, and performance
expectations.

6. Implement and maintain CI/CD and version control best practices
Design, implement, and support
robust CI/CD pipelines to automate build, test, deployment, and release processes for data pipelines, database objects, and cloud
infrastructure. Enforce effective version control practices using Git-based workflows, including branching strategies, pull requests, code
reviews, and release management. Promote automated testing, infrastructure as code (IaC), and deployment standards to ensure consistency,
traceability, reliability, and rapid, low-risk delivery across environments.

7. Develop and support data pipelines for
business intelligence and analytics
Design, build, and maintain reliable, scalable data pipelines that deliver curated,
analytics-ready datasets to support Business Intelligence and reporting needs.
Implement transformation logic, data validation checks,
and orchestration workflows to ensure accuracy, consistency, and timely data availability. Proactively monitor pipeline performance,
troubleshoot data issues, and optimize data flows to support dashboards, KPI tracking, ad hoc analysis, and
enterprise reporting requirements.

8. Support and implement data security and compliance requirements
Partner
with operations and security teams to implement and maintain data security controls, access policies, encryption standards, and compliance
requirements to safeguard sensitive and regulated data.

9. Monitor, troubleshoot, and enhance pipeline performance

Continuously monitor data workflows, resolve data processing issues, identify bottlenecks, and enhance performance across ETL/ELT processes, pipelines, and data integrations.

10. Gather requirements
and document data workflows
Collaborate with business stakeholders to collect requirements for data pipelines, integrations,
and reporting needs. Document data processes, transformation logic, workflow designs, and operational procedures for cross-team visibility
and long-term maintainability.

11. Operate effectively both independently and within cross-functional
teams
Demonstrate the ability to manage priorities, drive initiatives, and deliver high-quality solutions independently while
also contributing collaboratively within cross-functional teams. Engage proactively with engineering, BI, security, operations, and business
stakeholders to align on requirements, resolve issues, and deliver integrated data solutions. Communicate clearly, share knowledge, and
support team objectives to ensure successful project outcomes and continuous improvement.

Learn more about the great benefits
of working for University of Utah: benefits.utah.edu

The department may choose to hire at any of the below job
levels and associated pay rates based on their business need and budget.

Responsibilities
Data Engineer,
IIIDesign, build, implement, and maintain data processing pipelines for the extraction, transformation, and loading (ETL) of data
from a variety of data sources. Develop robust and scalable solutions that transform data into a useful format for analysis, enhance data
flow, and enable end users to consume and analyze data faster and easier. Write complex SQL queries to support analytics needs. Evaluate and
recommend tools and technologies for data infrastructure and processing. Collaborate with engineers, data scientists, data analysts, product
teams, and other stakeholders to translate business requirements to technical specifications and coded data pipelines. Work with tools,
languages, data processing frameworks, and databases such as R, Python, SQL, MongoDB, Redis, Hadoop, Spark, Hive, Scala, BigTable,
Cassandra, Presto, Strom. Work with structured and unstructured data from a variety of data stores, such as data lakes, relational database
management systems, and/or data warehouses. Considered highly skilled and proficient in discipline. Conduct complex, important work under
minimal supervision and with wide latitude for independent judgment.

Requires a bachelor’s (or equivalency) + 6 years or a master’s
(or equivalency) + 4 years of directly related work experience.

This is a Career-Level position in the General Professional track.Job
Code: P34033Grade: P21Expected Pay Range: $99,858 to $124,278

Minimum Qualifications
EQUIVALENCY
STATEMENT: 1 year of higher education can be substituted for 1 year of directly related work experience (Example: bachelor’s degree
= 4 years of directly related work experience).
Department may hire employee at one of the following job levels:
Data Engineer, III: Requires a bachelor’s (or equivalency) + 6 years or a master’s (or equivalency) + 4 years of directly
related work experience.

Preferences
Applicants will be screened according to preferences.

Experience with
cloud data services (AWS preferred: Glue, S3, EC2; bonus for Lambda, Athena, EMR)Familiarity with building and maintaining data
pipelines and integrations in cloud environments. Strong experience with Microsoft SQL Server and T-SQLProficiency in
writing, optimizing, and troubleshooting complex queries, stored procedures, and database objects.
Development experience in
Python for data engineering Hands-on experience using Python libraries such as Pandas, PySpark, or Boto3 for data processing,
automation, or integrations. Experience with version control and CI/CD tools (Git, GitHub/GitLab, Jenkins, etc.) Ability
to build and maintain automated deployment workflows for data pipelines. Ability to read or understand Java is a
plusHelpful for working with legacy connectors, middleware, or JVM-based big data tools. Experience with data
visualization/reporting tools (Power BI, Tableau, SSRS)Ability to support analytics teams by preparing data structures suitable for
reporting and dashboarding. Understanding of data warehouse principles (Star/Snowflake schemas)Knowledge of how to
structure data for analytics and reporting, even if the primary focus is pipeline engineering. Working knowledge of database
management, data integration patterns, and ETL/ELT frameworksComfortable working with relational, cloud, and distributed data
platforms. Strong analytical and problem-solving skillsAbility to diagnose data issues, performance bottlenecks, and
pipeline failures. Strong communication skillsCapable of explaining data concepts and pipeline logic to developers,
analysts, and non-technical stakeholders. Experience working in Agile environmentsProven ability to meet deadlines,
prioritize tasks, and deliver high-quality solutions in iterative development cycles.

Special
Instructions

Requisition Number: PRN45144B
Full Time or Part Time? Full Time
Work Schedule Summary:
Work ScheduleFull-time, 40 hours per week. Monday – Friday from 8:00 am to 5:00 pmWork Location & ResidencyThis position offers a flexible,
mostly remote work schedule for candidates who reside in the state of Utah. While most duties can be performed remotely, the employee must
be available to attend essential meetings and events on campus as needed.Work ProfileHybrid WorkA hybrid telework schedule is available for
this position, dependent on operational needs and management approval. The arrangement will be established in partnership with the manager
and is subject to ongoing departmental needs.Travel:This position may require occasional travel.
Department: 02228 – Data
Coordinating Center
Location: Campus
Pay Rate Range: 99858 to 124278
Close Date: 8/20/2026
Open Until
Filled:

To apply, visit https://utah.peopleadmin.com/postings/202465

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