Senior Data Engineer

Fictiv•Schaumburg, IL

2h•$167,000 - $202,300

About The Position

MISUMI Americas is seeking a Senior Data Engineer to serve as the primary owner of its Snowflake warehouse, dbt project, and data pipelines. The role transforms data from business and production systems into trusted, analytics-ready models used by Operations, Finance, Sales, Marketing, Engineering, Product, and Customer Support. The role’s judgment and execution affect the accuracy, security, and reliability of the data platform, including regulated data such as CUI. The engineer works hands-on with Snowflake, dbt, Fivetran, Git/GitHub, and AI coding agents. They independently take work from intake through production, help determine what to build next, and shape safe practices for agentic development. They partner with cross-functional and remote teams, translate business needs into technical models and specifications, and advise stakeholders on design decisions and trade-offs. The position also maintains and contributes to the Quality Management System (QMS). Travel of 5% annually in the US is required for team meetings, offsites, and conferences. The stated base salary range is $167,000–$202,300 per year, depending on experience.

Requirements

Bachelor’s degree in Data Science, Computer Science, Information Systems, or a related field, or equivalent practical experience.
10+ years of experience as a data engineer designing, building, and operating data pipelines and warehouses.
6+ years of hands-on production Snowflake experience, including administration, access control, warehouse management, and cost and performance monitoring.
5+ years building and maintaining production dbt projects (dbt Core or dbt Cloud), including testing, documentation, incremental models, macros, and CI/CD.
5+ years working with ETL/ELT tools such as Fivetran, Airbyte, Stitch, Matillion, Informatica, or Talend, plus pipeline orchestration experience.
Hands-on daily use of agentic coding tools that work directly in the codebase, not chat-only AI use, including building agentic workflows or tool integrations for data work and providing concrete examples of workflows and results.
Expert SQL, including CTEs, window functions, and performance tuning.
Daily use of Git and GitHub pull-request workflows.
Experience as the primary or sole owner of a data platform, or as its technical lead.
Experience administering data platforms containing regulated data, such as CUI under NIST SP 800-171, including access control, audit logging, encryption, and data classification.
Ability to pass a background check and complete required CUI and security training.
Direct, hands-on production experience in Snowflake administration and engineering, including access control, warehouse sizing and cost control, query tuning, clustering, security controls, cloning, time travel, and data sharing.
Direct, hands-on production experience with dbt modeling, including layered and dimensional models, incremental models, snapshots, macros, tests, documentation, and Git-based CI/CD.
Direct, hands-on production experience with ETL/ELT tooling, including connector setup, schema drift, deleted records, backfills, and cost considerations.
Ability to write advanced SQL on large, complex datasets, including semi-structured data such as JSON in Snowflake VARIANT columns.
Ability to apply data warehousing and dimensional modeling principles to create well-structured, maintainable models from business requirements.
Ability to independently define an approach to an open-ended problem, deliver it, and raise risks early.
Sound production judgment, including verifying before changing shared systems, planning for failure, and preparing rollback plans.
Disciplined use of AI tools: provide clear context and constraints, review and test every AI-generated change, and remain accountable for the result.
Methodical problem-solving skills, including developing testable hypotheses, gathering evidence from logs, lineage, and query history, confirming causes, and verifying fixes.
Clear written and verbal communication, with the ability to provide business context to engineers and technical context to non-engineers.
Ability to travel 5% annually in the US for team meetings, offsites, and conferences.

Nice To Haves

Direct Fivetran experience, including connector management.
Experience with Sigma Computing, Tableau, Looker, or a similar BI tool.
Experience modeling ERP and CRM data, such as NetSuite or Salesforce.
Python experience for data tooling and automation.
Snowflake SnowPro or dbt certification.
Infrastructure-as-code experience for Snowflake, such as Terraform.
Background in manufacturing, supply chain, or marketplace businesses.

Responsibilities

Administer Snowflake users, roles, grants, warehouses, resource monitors, and security policies while maintaining a secure, performant, and cost-efficient environment.
Design, build, and own dbt staging, intermediate, mart, and semantic-layer models, including tests and documentation.
Build, operate, and monitor ELT pipelines from production and business systems into Snowflake; resolve connector failures, schema changes, backfills, and data-quality issues.
Serve as the primary technical resource for data modeling and pipeline work, taking requests from intake to production, including scoping, estimating, and status reporting.
Translate business requirements into technical requirements and data models; clarify needs, agree on metric definitions and acceptance criteria, and create buildable and testable specifications with stakeholders.
Identify and propose data, modeling, and automation opportunities, and prioritize them with stakeholders.
Conduct impact analysis before changing, deprecating, or removing shared tables, models, or connectors using dbt lineage, Snowflake dependency and access metadata, and stakeholder review; document rollback plans.
Own Git and GitHub workflows for the dbt project, including branching standards, pull-request review, automated tests, and deployment.
Use AI assistants and agentic workflows for SQL and dbt development, testing, documentation, code review, and debugging, and help establish safe team practices for their use.
Find root causes of data-quality, performance, and pipeline issues and address them at the source.
Tune queries, models, and warehouse usage; advise engineers and analysts on efficient query patterns and table design.
Maintain data dictionaries, data-flow diagrams, and schema documentation.
Lead data design sessions and technical reviews, explaining design trade-offs to technical and non-technical stakeholders.
Evaluate Snowflake and dbt features and other tools, and recommend platform improvements.
Mentor analysts and engineers on SQL and dbt best practices.
Work with remote, cross-functional teams across time zones.
Maintain and contribute to the Quality Management System (QMS).