Senior Data Engineer at Fictiv

Job description

About MISUMI Americas 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. Senior Data Engineer - Schaumburg MISUMI | Data Platform Own the data platform that MISUMI runs on 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/GitHub) 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. Impact in this Role 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. Essential Functions 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 GitHub 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 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. 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 new Snowflake and dbt features and tools, and recommend improvements to the data platform. 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). Desired Traits Five core skills. Candidates must show direct, hands-on production experience in each: Snowflake administration and engineering: access control, warehouse sizing and cost control, query tuning, clustering, security controls, cloning, time travel, and data sharing. dbt expert modeling: 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 (our tool) or similar, including connector setup, schema drift, deleted records, backfills, and cost. Agentic coding and development: daily use of AI coding agents that work in the repository and terminal (not chat-only use), with disciplined verification of everything they produce. Required 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 Snowflake experience in production, 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 experience with pipeline orchestration. Demonstrated, hands-on experience using agentic coding tools that work directly in the codebase (not chat-only AI use) in daily data engineering work. This includes 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 the workflows and results. Expert SQL, including CTEs, window functions, and performance tuning. Daily use of Git and GitHub pull-request workflows. Experience being the primary or sole owner of a data platform, or the technical lead for one. Experience administering data platforms that hold regulated data (such as CUI under NIST SP 800-171), including access control, audit logging, encryption, and data classification. Must be able to pass a background check and complete required CUI and security training. Preferred Direct Fivetran experience, including connector management. Experience with Sigma Computing, or with Tableau, Looker, or a similar BI tool. Experience modeling ERP and CRM data, such as NetSuite or Salesforce. Python 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. Abilities Write advanced SQL on large, complex datasets, including semi-structured data such as JSON in Snowflake VARIANT columns. Apply data warehousing and dimensional modeling principles to turn business requirements into well-structured, maintainable models. Work independently: take an open-ended problem, define the approach, deliver it, and raise risks early instead of stalling. Use sound production judgment: verify before changing shared systems, plan for failure, and know how to roll back. Use AI tools with discipline: give clear context and constraints, review and test every AI-generated change, and stay accountable for the result. Solve hard problems methodically: break an unclear problem into testable hypotheses, gather evidence from logs, lineage, and query history, confirm the cause before changing anything, and verify the fix worked. Communicate clearly in writing and in person, giving business context to engineers and technical context to non-engineers. Physical Demands The employee routinely sits, talks, hears, and uses hands and fingers to operate a keyboard. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Travel Ability to travel 5% annually in the US for team meetings, offsites, and conferences. Ready to own the platform? If you have run production Snowflake, dbt, and ELT at scale, you work daily with agentic coding tools, and you want to be the person the business trusts with its data, we would like to hear from you. Apply today. Salary range: $167,000 - $202,300 base salary per year, depending on experience Benefits include, but are not limited to: Comprehensive medical and dental coverage 401(k) plan Generous PTO and leave programs We’re actively seeking teammates who: Bring diverse perspectives and experience to our culture and company. Excel at being part of a strong, empathetic team. Thrive in an environment emphasizing respect, honesty, collaboration, and growth. Have an ‘always learning’ mindset that celebrates learning, not just wins. Help us continue to build a world-class organization that values the contributions of all of our teammates We encourage applications from members of underrepresented groups, including but not limited to women, members of the LGBTQ community, people of color, people with disabilities, and veterans.

Requirements

Must-haves

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.
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 GitHub 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 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.
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.

Nice-to-haves

Direct Fivetran experience, including connector management.
Experience with Sigma Computing, or with Tableau, Looker, or a similar BI tool.
Experience modeling ERP and CRM data, such as NetSuite or Salesforce.
Python 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.
Write advanced SQL on large, complex datasets, including semi-structured data such as JSON in Snowflake VARIANT columns.
Apply data warehousing and dimensional modeling principles to turn business requirements into well-structured, maintainable models.
Work independently: take an open-ended problem, define the approach, deliver it, and raise risks early instead of stalling.
Use sound production judgment: verify before changing shared systems, plan for failure, and know how to roll back.
Use AI tools with discipline: give clear context and constraints, review and test every AI-generated change, and stay accountable for the result.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

goaisqlci/cdpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto