Senior Data Engineer, Fictiv

• 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
• 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
• 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
• 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

Desirable

• 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 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
• 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 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)