Senior Data Engineer Schaumburg, IL

MISUMI Americas, a division of MISUMI Group, is a leading provider of standard, configurable, and custom manufacturing solutions. By integrating a vast catalog of components with a world-class digital manufacturing platform, MISUMI Americas empowers engineers and procurement teams to accelerate innovation across the entire product lifecycle. With operations in the San Francisco Bay Area and Chicago, the company serves as a vital partner for the most innovative companies in the Americas.

MISUMI | Data Platform

MISUMI is looking for a Senior Data Engineer to be the go-to owner of our Snowflake warehouse, dbt project, and data pipelines. You will be the primary data engineer, with real ownership and real influence: the models you build and the pipelines you run are what Operations, Finance, Sales, Marketing, Engineering, Product, and Customer Support rely on to make decisions every day.

You will work hands-on with a modern stack (Snowflake, dbt, Fivetran, Git/Git Hub) and with AI coding agents in your daily engineering work. You will also have the autonomy to take requests from intake to production, propose what we should build next, and shape safe team practices for agentic development. If you want a role where your judgment directly determines whether the business can trust its data, this is it.

The Senior Data Engineer is the go-to owner of MISUMI’s Snowflake warehouse, dbt project, and data pipelines. This person turns data from business and production systems into trusted, analytics-ready models that Operations, Finance, Sales, Marketing, Engineering, Product, and Customer Support use to make decisions. Because this is the primary data engineering role, the accuracy, security, and reliability of the data platform, including regulated data such as CUI, depend on this person’s judgment and execution.

• Administer Snowflake: manage users, roles, grants, warehouses, resource monitors and security policies, and keep the environment secure, fast and cost-efficient.
• Design, build, and own dbt models (staging, intermediate, marts, and the semantic layer) that turn replicated source data into trusted, analytics-ready tables with tests and documentation.
• Build, operate, and monitor ELT pipelines from production and business systems into Snowflake, using Fivetran and related tools. Resolve connector failures, schema changes, backfills, and data-quality issues.
• Act as the team’s primary technical resource for modeling and pipeline work. Take requests from intake to production independently, including scoping, estimating, and reporting status.
• Translate business requirements into technical requirements and data models, and bridge the gap between business teams and technology. Ask clarifying questions, agree on metric definitions and acceptance criteria with stakeholders, and turn them into specifications the team can build and test.
• Proactively identify opportunities where data, modeling, or automation can help the business get closer to its goals, and propose and prioritize them with stakeholders instead of waiting for requests.
• Run an impact analysis before changing, deprecating, or removing any shared table, model, or connector. Use dbt lineage, Snowflake dependency and access metadata, and stakeholder review, and document a rollback plan.
• Own the Git and Git Hub workflow for the dbt project: branching standards, pull-request review, automated tests, and deployment.
• Use AI assistants and agentic workflows in daily engineering work to speed up SQL and dbt development, testing, documentation, code review, and debugging, and help set safe team practices for using them.
• Find the root cause of data-quality, performance, and…