Data Engineer (Hybrid) at Office of University Development, University of Michigan

Wayne State University; Ann Arbor (MI); On-site; USD 80,000 - 100,000; Full time

Job summary

A prestigious educational institution in Ann Arbor seeks a Data Engineer to wrangle messy data and create structured data products. This role demands strong programming skills in SQL and Python, along with experience in big data storage formats like Hadoop and Databricks. The ideal candidate will have over 3 years of relevant experience and the ability to communicate technical concepts effectively to diverse stakeholders. Join a team dedicated to using data for positive philanthropic impact.

Qualifications

• 3+ years of experience with 'big data' data storage formats and query tools.
• 2+ years of experience with modern data engineering workflows.
• Demonstrated ability to build relationships and communicate concepts.

Responsibilities

• Wrangle and clean messy data from various sources.
• Build structured data products for analytics and machine learning.
• Collaborate with stakeholders to translate requirements into technical solutions.

Skills

Data munging

Data wrangling

SQL

Python

Building relationships

Communication

Hadoop

Databricks

AI integration

Data visualization

Education

Bachelor's degree in information systems, computer science/engineering or data science

Tools

Amazon AWS

Databricks

Hadoop

Hive

Parquet

Delta Lake

NoSQL databases

Job description

To be considered, you must be legally authorized to work in the United States without the need for employer sponsorship now or at any time in the future.

Our new location: Michigan House, 2301 Commonwealth Blvd, Ann Arbor, MI 48105.

Reports to: Director of Data Science and Decision Support.

What you will do

We are a small team of data lovers using data for the common good, helping the University of Michigan improve our world through data-informed philanthropy. We not only transform data into action, we do so mindfully, maintaining a holistic commitment to inclusion in all that we do. As our new data engineer, you will wrangle messy and complicated data. You will also pull data from APIs, take advantage of AI tools, and build structured data products, used in machine learning and artificial intelligence-based tools for university fundraising. Because we operate in a highly collaborative, mission-driven environment, success in this role requires strong relationship-building skills and the ability to communicate complex technical concepts clearly to a variety of audiences – from fellow data practitioners to non-technical partners. You will work closely with stakeholders to understand needs, translate business challenges into scalable technical solutions, and adapt your communication style to meet people where they are.

A typical work week will include a large amount of programming and data pipeline development, along with meaningful collaboration, data management, requirements gathering, documentation, outreach, brainstorming sessions, and data visualization. You will play an active role in strengthening cross-functional partnerships and amplifying the impact of our work across the university community.

Required Qualifications

• Bachelor's degree in information systems, computer science/engineering or data science field.
• The ability to do serious data munging and data wrangling is important. Demonstrated experience with SQL plus knowledge of Python. Using these tools to merge large datasets, locate and clean messy records, recode missing values, convert between data formats, and construct standardized datasets for wider use.
• Demonstrated ability to build strong working relationships and communicate complex technical concepts clearly to both technical and non-technical stakeholders.
• 3+ years of experience with "big data" data storage formats and query tools (e.g., Hadoop, Hive, Parquet, Delta Lake, NoSQL databases).
• 2+ years of experience with each of the following:
• Amazon AWS and Databricks
• Modern data engineering workflows and frameworks, including gathering requirements and translating business needs into scalable data solutions
• Software development best practices (e.g., Agile methods and unit testing) and version control tools
• Integrate AI tools into data engineering workflow
• Develop interactive, reusable and self-service online analytics tools
• Machine learning and data mining concepts and techniques
• Cloud-based analytics tools