Senior Data Engineer

Fictiv•Schaumburg, IL

3d•$167,000 - $202,300

About The Position

MISUMI Americas is seeking a Senior Data Engineer to own its Snowflake warehouse, dbt project, and data pipelines. This is the primary data engineering role, responsible for turning data from business and production systems into trusted, analytics-ready models used by Operations, Finance, Sales, Marketing, Engineering, Product, and Customer Support. The role has direct influence over the accuracy, security, and reliability of the data platform, including regulated data such as CUI. The Senior Data Engineer works hands-on with Snowflake, dbt, Fivetran, Git/GitHub, and AI coding agents. The position combines platform administration, modeling and pipeline engineering, stakeholder engagement, independent delivery from request intake through production, and development of safe practices for agentic coding. The role also supports cross-functional teams across time zones, contributes to the Quality Management System, and mentors analysts and engineers.

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.
Direct, hands-on production experience with Snowflake administration and engineering, including access control, warehouse sizing and cost control, query tuning, clustering, security controls, cloning, time travel, and data sharing.
Expert dbt modeling experience with layered and dimensional models, incremental models, snapshots, macros, tests, documentation, and Git-based CI/CD.
Expert SQL, including CTEs, window functions, recursive queries, semi-structured data, and performance tuning.
Daily use of Git and GitHub pull-request workflows.
Demonstrated hands-on daily use of agentic coding tools that work directly in the codebase, not chat-only AI use, with disciplined verification of their output.
Experience building agentic workflows or tool integrations for data work, such as MCP servers, custom agents, or automated dbt and pipeline checks, with concrete examples of workflows and results.
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.
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 maintainable models from business requirements.
Ability to work independently on open-ended problems, define an approach, deliver it, and raise risks early.
Ability to exercise sound production judgment, verify before changing shared systems, plan for failure, and know how to roll back.
Ability to use AI tools responsibly by providing clear context and constraints, reviewing and testing generated changes, and remaining accountable for results.
Ability to solve problems methodically using evidence from logs, lineage, and query history, and to verify that fixes work.
Clear written and verbal communication, including the ability to provide business context to engineers and technical context to non-engineers.

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 certification 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, cost-efficient environment.
Design, build, and own dbt staging, intermediate, mart, and semantic-layer models, including their 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.
Serve as the primary technical resource for modeling and pipeline work, taking requests independently from intake through production, including scoping, estimating, and status reporting.
Translate business requirements into technical requirements, data models, specifications, metric definitions, and acceptance criteria in collaboration with stakeholders.
Identify and propose data, modeling, and automation opportunities, and prioritize them with stakeholders.
Conduct impact analyses before changing, deprecating, or removing shared tables, models, or connectors; use lineage, dependency and access metadata, stakeholder review, and documented rollback plans.
Own the dbt project's Git and GitHub workflow, including branching standards, pull-request review, automated tests, and deployment.
Use AI assistants and agentic workflows in daily engineering work for SQL and dbt development, testing, documentation, code review, and debugging; help establish safe team practices.
Find and resolve root causes of data-quality, performance, and pipeline problems, including fixing issues at their source.
Tune queries, models, and warehouse usage, and 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 and explain design trade-offs to technical and non-technical stakeholders.
Evaluate Snowflake and dbt features and other tools, and recommend data-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).