Data Engineer Senior

University of Michigan

Ann Arbor, MI

Full Time

Senior

26 days ago

Job Description

About the Role

The University of Michigan's Data Science Practice team is seeking a Senior Data Engineer to help lead our modernization journey. As stewards of the University's enterprise data assets, our team is reimagining the future of data infrastructure to support advanced analytics, data science, Artificial Intelligence, and institutional decision-making. This role presents a unique opportunity to contribute to shaping our new cloud-based, scalable, and secure data ecosystem. You'll collaborate with a highly motivated team working at the intersection of data engineering, strategy, and analytics. Together, we are building the foundation for the next generation of data-driven insights at a world-class public research university.

Key Responsibilities

Design, implement, and optimize data pipelines that support the university's modernization strategy.
Contribute to the design of our future cloud-native data architecture and the selection and implementation of our new toolset.
Collaborate with data scientists, analysts, and stakeholders to deliver high-quality, well-documented data products.
Develop scalable ETL/ELT processes that integrate data from enterprise systems (e.g., PeopleSoft, Salesforce, Blackbaud).
Establish and promote best practices for data modeling, engineering, and governance in a modern data platform environment.
Lead and mentor peers and junior engineers as part of a collaborative, high-performing team.
Support agile delivery and CI/CD practices to improve efficiency and quality across the data lifecycle.

Requirements

Bachelor's degree in a related field and/or equivalent combination of education, certification, and experience.
5+ years of professional experience in data engineering, software development, or a related discipline.
Strong proficiency in SQL and experience with both relational and non-relational database systems.
Experience developing in Python and/or Scala, particularly for data pipeline creation and automation.
Hands-on experience with cloud environments (AWS, Azure, or GCP) and associated data services.
Experience with big data frameworks such as Apache Spark or Databricks.
Knowledge of workflow orchestration tools (e.g., Apache Airflow, Prefect, or similar).
Familiarity with modern CI/CD and infrastructure-as-code practices (e.g., Terraform, GitHub Actions).
Strong understanding of data architecture, integration, and governance principles.
Excellent communication skills and ability to collaborate in a team-oriented environment.

Nice to Have

Master's degree in a relevant field (e.g., Data Science, Computer Science, Engineering).
Experience supporting data science and machine learning workflows.
Familiarity with modern data stack tools such as dbt, Snowflake, Fivetran, or Redshift.
Understanding of data privacy and compliance frameworks (e.g., FERPA, HIPAA, GDPR).
Experience modernizing legacy data systems or migrating to cloud-native architectures.
Knowledge of ML Ops, feature engineering, or model deployment tools.
Experience mentoring team members or leading technical projects.
Background in higher education or research environments is a plus.