Data Engineer, AI Development
Ultimus Fund Solutions
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- Pay
- $100K to $250K
- a year
- Location
- Omaha, NE
- Work style
- On-site
- Job type
- Other
- Posted
- Nov 7
- 1 day ago
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Career path
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About the company
Ultimus Fund Solutions
Financial services · 501 to 1,000 employees
What you'll do
- Map the data each workflow uses today, including systems, files, emails and spreadsheets
- Design how AI solutions will get workflow data
- Build workflow-specific pipelines, integrations and data models using Snowflake and the shared AI platform
- Turn unstructured inputs into structured data that models and agents can use
- Set up data quality checks and fix data issues found during testing
- Contribute workflow data back to the shared platform for reuse
Key skills
Snowflakedata modelsdata quality checksstructured data
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About the team
Ultimus is investing in AI to transform how fund administration work gets done, from generative AI assistants, intelligent document processing and machine learning to a growing set of agentic AI workflows. Our AI Development teams are small, cross-functional groups of AI engineers, data scientists, data engineers and AI analysts who partner closely with our operations experts to redesign key workflows and build the AI solutions that support them, with AI agents an increasingly important part of that work. They are backed by a dedicated AI Infrastructure team and a shared enterprise AI platform.
About the role
As Data Engineer in an AI Development pod you make sure each workflow's AI solutions, including models, analytics and AI agents, have the right data, in the right shape, at the right time. You work on the specific data each workflow needs, building on the shared platform from the AI Infrastructure team.
What you'll do
Map the data each workflow uses today, including systems, files, emails and spreadsheets, and design how AI solutions will get it.
Build the workflow-specific pipelines, integrations and data models that AI solutions need, using Snowflake and the shared AI platform.
Turn unstructured inputs such as statements, subscription documents and Excel workbooks into structured data that models and agents can use.
Set up data quality checks so AI solutions work from accurate, complete data, and fix data issues found during testing.
Contribute workflow data back to the shared platform so the next workflow can reuse it.