{"id":3131678,"url":"https://alion.io/job/fictiv-senior-data-engineer","title":"Senior Data Engineer","company":{"id":1859,"name":"Fictiv","domain":"fictiv.com","url":"https://alion.io/company/fictiv","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":72,"open_postings":18,"ghost_share":0.056,"stale_share":0.944,"repost_share":0.056,"time_to_fill_p50_days":49,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Schaumburg, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":167000,"max":202300,"currency":"USD","period":"year","gross":null,"usd_annual":202300},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"Informatica","optional":false},{"name":"Looker","optional":false},{"name":"Matillion","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Talend","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-23T02:57:21Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-28T23:51:06Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About MISUMI Americas\nMISUMI 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.\nSenior Data Engineer - Schaumburg\nMISUMI | Data Platform\nOwn the data platform that MISUMI runs on\nMISUMI 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.\nYou 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.\nImpact in this Role\nThe 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.\nEssential Functions\nAdminister Snowflake: manage users, roles, grants, warehouses, resource monitors and security policies, and keep the environment secure, fast and cost-efficient.\nDesign, 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.\nBuild, 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.\nAct 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.\nTranslate 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.\nProactively 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.\nRun 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.\nOwn the Git and GitHub workflow for the dbt project: branching standards, pull-request review, automated tests, and deployment.\nUse 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.\nFind the root cause of data-quality, performance, and pipeline problems and fix them at the source.\nTune queries, models, and warehouse usage, and advise engineers and analysts on efficient query patterns and table design.\nMaintain data dictionaries, data-flow diagrams, and schema documentation.\nLead data design sessions and technical reviews, and explain design trade-offs to technical and non-technical stakeholders.\nEvaluate new Snowflake and dbt features and tools, and recommend improvements to the data platform.\nMentor analysts and engineers on SQL and dbt best practices.\nWork with remote, cross-functional teams across time zones.\nMaintain and contribute to the Quality Management System (QMS).\nDesired Traits\nFive core skills. Candidates must show direct, hands-on production experience in each:\nSnowflake administration and engineering: access control, warehouse sizing and cost control, query tuning, clustering, security controls, cloning, time travel, and data sharing.\ndbt expert modeling: layered models, dimensional modeling, incremental models, snapshots, macros, tests, documentation, and CI/CD through Git.\nAdvanced SQL: CTEs, window functions, recursive queries, semi-structured data, and query tuning.\nETL/ELT tooling: Fivetran (our tool) or similar, including connector setup, schema drift, deleted records, backfills, and cost.\nAgentic 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.\nRequired\nBachelor’s degree in Data Science, Computer Science, Information Systems, or a related field, or equivalent practical experience.\n10+ years of experience as a data engineer, designing, building, and operating data pipelines and warehouses.\n6+ years of hands-on Snowflake experience in production, including administration: access control, warehouse management, and cost and performance monitoring.\n5+ years building and maintaining production dbt projects (dbt Core or dbt Cloud), including testing, documentation, incremental models, macros, and CI/CD.\n5+ years working with ETL/ELT tools such as Fivetran, Airbyte, Stitch, Matillion, Informatica, or Talend, plus experience with pipeline orchestration.\nDemonstrated, 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.\nExpert SQL, including CTEs, window functions, and performance tuning.\nDaily use of Git and GitHub pull-request workflows.\nExperience being the primary or sole owner of a data platform, or the technical lead for one.\nExperience 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.\nPreferred\nDirect Fivetran experience, including connector management.\nExperience with Sigma Computing, or with Tableau, Looker, or a similar BI tool.\nExperience modeling ERP and CRM data, such as NetSuite or Salesforce.\nPython for data tooling and automation.\nSnowflake SnowPro certification or dbt certification.\nInfrastructure-as-code experience for Snowflake, such as Terraform.\nBackground in manufacturing, supply chain, or marketplace businesses.\nAbilities\nWrite advanced SQL on large, complex datasets, including semi-structured data such as JSON in Snowflake VARIANT columns.\nApply data warehousing and dimensional modeling principles to turn business requirements into well-structured, maintainable models.\nWork independently: take an open-ended problem, define the approach, deliver it, and raise risks early instead of stalling.\nUse sound production judgment: verify before changing shared systems, plan for failure, and know how to roll back.\nUse AI tools with discipline: give clear context and constraints, review and test every AI-generated change, and stay accountable for the result.\nSolve 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.\nCommunicate clearly in writing and in person, giving business context to engineers and technical context to non-engineers.\nPhysical Demands\nThe 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.\nTravel\nAbility to travel 5% annually in the US for team meetings, offsites, and conferences.\nReady to own the platform?\nIf 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.\nSalary range: \n$167,000 - $202,300 base salary per year, depending on experience\nBenefits include, but are not limited to:\nComprehensive medical and dental coverage\n 401(k) plan\n Generous PTO and leave programs\nWe’re actively seeking teammates who:\nBring diverse perspectives and experience to our culture and company. \nExcel at being part of a strong, empathetic team.\nThrive in an environment emphasizing respect, honesty, collaboration, and growth.\nHave an ‘always learning’ mindset that celebrates learning, not just wins. \nHelp us continue to build a world-class organization that values the contributions of all of our teammates\nWe 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.","description_format":"text","description_chars":9666,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Contract Manufacturing","Manufacturing & Industrial Software"],"lifecycle":[{"event":"open","at":"2026-10-23T03:58:45Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 3","filings_12m":3,"filings_prev_12m":5,"green_card_filings_12m":0,"median_offered_wage_usd":162500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":0}],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":5,"expected_fill_days":49,"reasons":["conf:3","stale_co","velocity","win:early"],"computed_at":"2026-10-28T05:59:10Z"},"pay":{"stated_usd_annual":202300,"is_top_pay":true},"html_url":"https://alion.io/job/fictiv-senior-data-engineer","json_url":"https://alion.io/job/fictiv-senior-data-engineer.json","meta":{"generated_at":"2026-10-29T01:48:49Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":2739,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-30T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1859},"rest":"https://alion.io/mcp/rest/get_company?id=1859"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ffictiv-senior-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ffictiv-senior-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ffictiv-senior-data-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/fictiv-senior-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ffictiv-senior-data-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3131678}},{"tool":"search_jobs","arguments":{"company":"fictiv.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3131678},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}