Senior Data Engineer

Description

The company is a division of a larger manufacturing group. It supplies standard, configurable, and custom manufacturing solutions, combining a large component catalog with a digital manufacturing platform to help engineers and procurement teams move products forward faster.

The company has offices in the San Francisco Bay Area and Chicago and works with innovative businesses across the Americas.

This role is based in Schaumburg and sits on the data platform team as the main data engineer, owning the Snowflake warehouse, the dbt project, and the data pipelines.

Operations, Finance, Sales, Marketing, Engineering, Product, and Customer Support depend on these models and pipelines every day to make decisions, so the work directly affects whether the business can trust its data.

The platform includes regulated data such as CUI, so accuracy, security, and reliability depend heavily on this role's judgment and execution.

The team works with Snowflake, dbt, Fivetran, Git and GitHub, and AI coding agents. You would take requests from start to finish, suggest what to build next, and help define safe practices for agentic development.

Responsibilities

• Manage the Snowflake environment, including users, roles, grants, warehouses, resource monitors, and security policies, keeping it secure, fast, and cost-efficient.
• Build and maintain dbt models across staging, intermediate, and mart layers and the semantic layer, with tests and documentation, so source data becomes analytics-ready tables.
• Build, run, and monitor ELT pipelines from production and business systems into Snowflake using Fivetran and similar tools. Fix connector failures, schema changes, backfills, and data-quality problems.
• Serve as the main technical contact for modeling and pipeline work, taking requests through scoping, estimating, delivery, and status reporting on your own.
• Turn business needs into technical requirements and data models. Ask questions, agree on metric definitions and acceptance criteria with stakeholders, and write specifications the team can build and test.
• Look for chances to use data, modeling, or automation to move the business toward its goals, then propose and rank them with stakeholders without waiting to be asked.
• Assess impact before changing, deprecating, or removing any shared table, model, or connector, using dbt lineage, Snowflake's access and dependency records, and stakeholder review. Document a rollback plan.
• Take charge of the Git and GitHub workflow for the dbt project, covering branching standards, pull request review, automated tests, and deployment.
• Apply AI assistants and agent-driven workflows to SQL and dbt development, testing, documentation, review, and debugging, and help set safe team practices for using them.
• Identify root causes of data-quality, performance, and pipeline problems and fix them at the source.
• Tune queries, models, and warehouse usage, and advise engineers and analysts on efficient query patterns and table design.
• Keep data dictionaries, data-flow diagrams, and schema documentation current.
• Lead design sessions and technical reviews, and explain trade-offs to both technical and non-technical stakeholders.
• Evaluate new Snowflake and dbt features and tools and recommend platform improvements.
• Mentor analysts and engineers on SQL and dbt best practices.
• Collaborate with distributed, cross-functional groups in different time zones.
• Maintain and contribute to the Quality Management System (QMS).

Requirements

• A bachelor's degree in a field such as data science, computer science, or information systems, or equivalent hands-on experience.
• 10+ years creating, building, and running data pipelines and warehouses as a data engineer.
• 6+ years running Snowflake in production, including administration of access control, warehouses, and cost and performance monitoring.
• 5+ years building and maintaining production dbt projects (dbt Core or dbt Cloud), covering testing, documentation, incremental models, macros, and CI/CD.
• 5+ years with ETL/ELT tools such as Fivetran, Airbyte, Stitch, Matillion, Informatica, or Talend, plus pipeline orchestration experience.
• Hands-on use of agentic coding tools that work directly in the codebase, not chat alone, in daily data engineering. This includes building agentic workflows or tool integrations such as MCP servers, custom agents, or automated dbt and pipeline checks, with concrete examples of the workflows and their 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.
• Five core skills are required. Candidates must show direct, hands-on production experience in each:
• Snowflake administration and engineering: permissions, warehouse sizing and cost control, query tuning, clustering, security controls, cloning, time travel, and data sharing.
• dbt modeling at an expert level: layered models, dimensional modeling, incremental models, snapshots, macros, tests, documentation, and CI/CD through Git.
• Advanced SQL: CTEs, window functions, recursive queries, semi-structured data, and query tuning.
• ETL/ELT tooling: Fivetran or similar, including connector setup, schema drift, deleted records, backfills, and cost.
• Agentic coding: daily use of AI coding agents that work in the repository and terminal, not chat-only use, with careful verification of all output.
• Ability to write advanced SQL on large, complex datasets, including JSON in Snowflake VARIANT columns.
• Ability to apply warehousing and dimensional modeling principles to build well-structured, maintainable models from business requirements.
• Ability to work independently on open-ended problems: define the approach, deliver it, and raise risks early.
• Ability to make sound production decisions: check before changing shared systems, plan for failure, and know how to roll back.
• Ability to use AI tools carefully: give clear context and constraints, review and test every AI-generated change, and own the result.
• Ability to solve hard problems methodically: test hypotheses, use logs, lineage, and query history, confirm the cause before changing anything, and verify the fix.
• Ability to communicate clearly in writing and in person, giving business context to engineers and technical context to non-engineers.
• Preferred: hands-on Fivetran experience, including connector management.
• Preferred: experience with Sigma Computing, Tableau, Looker, or a similar BI tool.
• Preferred: experience modeling ERP and CRM data, such as NetSuite or Salesforce.
• Preferred: Python for data tooling and automation.
• Preferred: Snowflake SnowPro certification or dbt certification.
• Preferred: infrastructure-as-code for Snowflake, such as Terraform.
• Preferred: background in manufacturing, supply chain, or marketplace businesses.

Conditions

Base salary of $167,000 to $202,300 per year, depending on experience.

Based in Schaumburg.

Travel of up to 5% per year within the US for team meetings, offsites, and conferences.

The role involves sitting, talking, hearing, and keyboard use. Reasonable accommodations are available for people with disabilities who need them to perform the essential functions.

The company encourages applications from underrepresented groups, including women, LGBTQ people, people of color, people with disabilities, and veterans.

Benefits

• Comprehensive medical and dental coverage.
• 401(k) plan.
• Generous PTO and leave programs.