{"id":3418700,"url":"https://alion.io/job/rep-fitness-ai-analytics-engineer","title":"AI Analytics Engineer","company":{"id":687509,"name":"REP Fitness","domain":"repfitness.com","url":"https://alion.io/company/repfitness","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Westminster, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":81000,"max_usd":155000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":2036},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Context Engineering","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-28T17:14:01Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-30T06:30:09Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About REP: \nREP designs world-class, innovative gym equipment that is sold around the world, and we are now expanding into apparel, supplements, athletes, and events. REP was established in 2012 by two brothers who shared a deep passion for fitness. From our humble beginnings of selling bumper plates out of a small garage, REP has experienced remarkable growth, now occupying over 300,000+ square feet of office and distribution space in the USA and internationally. With a dedicated team of over 200+ fitness enthusiasts, our vision is to be the #1 Strength Brand in the World. Our commitment to innovation, customer service, and value sets us apart. \nWe believe in building a culture centered around fitness, so we specialize in outfitting home gyms, commercial gyms, and strength and conditioning facilities. Our dedication to excellence has been recognized multiple times when we were featured in the Inc. 5000 fastest growing private companies in 2018 and 2021 and then voted the best home strength product launch in 2022 by Garage Gym Experiment. Our team works out together, and we regularly invite fitness superstars to join us. We organize fun challenges to test our limits - and the limits of our equipment. \nAt REP, we continue to grow and bring new opportunities for those who want to be a part of a winning team. We firmly believe that being surrounded by like-minded individuals fuels creative energy and fosters a fun and collaborative work environment. This role is based in our state-of-the-art headquarters with a bright, spacious, shared workspace; scenic views of the Rocky Mountains; and a kitchen always stocked with healthy snacks, protein powder, and coffee. We offer a hybrid work environment with a minimum of three days in the office, but there’s flexibility about which days and hours.\nOur Core Values: \nThink Like a Customer - We empathize with our customers; understand the 'why'behind every decision we make \nProgress not Perfection - We don't let perfect get in the way of good; we are better today than we were yesterday \nPositive Attitude - Be a glass half-fullkind of person and remember to have fun \nTeamwork without Ego - We put the teamabove ourselves; we only win together \nTake Initiative - We drive momentum; if there is an opportunity to make us better, we want to hear it \nAbout This Role: \nWe have already decided to put AI in front of our data. This role decides whether we can trust what it says. \nREP’s analytics direction is MCP-first: our internal agents, ChatDash (internally built, conversational BI platform), and Claude now query our Microsoft Fabric data platform and other sources directly. Agents are only as accurate as the definitions underneath them. In addition to building pipelines to model data from sources into Fabric, the AI-Native Analytics Engineer builds the semantic models and context layers that let both people and AI agents get accurate, grounded answers from REP’s enterprise data. \nWhere a traditional analytics engineer stops at clean models for dashboards, this role extends the work into the agentic control plane: the certified metric definitions, data skills, evaluation suites, and guardrails that AI depends on to query data and act without hallucinating. And you will do it the AI-native way - using AI coding assistants to take new data from bronze to gold, with generated tests and automated reconciliation, compressing work that traditionally took a data engineer, a QA tester, and rounds of business UAT into one seat. What AI cannot do is the judgment: the spec, the review, and the call on whether the answer is correct. That is the job. \nJust as important: at REP, the business builds first. Our end users create working versions of the analytics they need on their own - in Claude or directly in ChatDash - often before certified data or clear semantics exist for that domain. You will not be writing requirements documents and building from scratch. The requirements are the thing the business has already built and is already using. Where certified data and semantics exist, your job is to make sure they are readily accessible through Claude, ChatDash, and other development approaches, so people build on trusted ground from the start. Where they do not, your job is to take the working version and figure out how to get its data flowing systemically, with clear semantics and guardrails, without breaking what the business made. \nThis is a senior individual contributor role with real ownership. You will own REP’s metric definitions of record, develop data pipelines according to a medallion architecture, direct our external data engineering partner as the technical owner, and work alongside our AI Solutions Engineer - they build the agents and tools; you build the governed data those agents rely on. \nEssential Functions: Include the following. Other duties may be assigned. \nBusiness-Built Solutions (Harden) \nTreat working versions built by business users in Claude and ChatDash as the requirements: reverse-engineer the questions, logic, sources, and implied metric definitions they encode \nTake those working versions and get their data flowing systemically - replacing ad-hoc extracts, uploads, and one-off queries with governed Fabric pipelines and certified semantics \nPreserve what the business built while hardening it: the user’s working version keeps working, now on trusted data with clear definitions and guardrails \nMake certified data and semantics accessible wherever people build - Claude, ChatDash, REP MCP, and other development approaches - so new work starts on governed ground \nPartner with the business builders directly, confirming that the hardened version answers the same questions they were answering before, and explaining any differences \nPipelines and Data Products (Build) \nBuild Fabric pipelines and gold data products end to end with AI-assisted development: ingestion into bronze, cleansing and conforming in silver, and gold tables and views ready for reporting and agents \nGenerate test suites and automated reconciliation against source-system reports, replacing most of the manual UAT burden on the business \nDerive the spec from the working version: the business builder is the named consumer, their use is the decision, and their current outputs become the acceptance criteria - with freshness targets and a business owner confirmed before productionizing \nKeep transformation code version-controlled, tested, and documented well enough that another engineer - or an agent - can extend it without asking \nSemantic Layer and Context Engineering (Trust) \nDeliver a certified semantic layer over REP’s Fabric gold layer - metrics, entity definitions, join paths, and grain rules - that REP MCP and ChatDash query by default, starting with inventory and net sales \nEstablish one definition of record per metric, retiring or explicitly mapping conflicting legacy definitions across Power BI, Polar, and NetSuite \nStructure business rules, metadata, and documentation as durable working memory for agents, not after-the-fact commentary \nResolve data-trust escalations at the root - in definitions and lineage - rather than through one-off reconciliations \nAI Answer Accuracy (Evaluate) \nStand up an evaluation framework: a question bank with known-correct answers and automated regression checks run on every semantic-layer or model change \nPublish a regular accuracy scorecard so leaders and the AI Governance committee know where agent answers can be trusted - and where they cannot \nCatch regressions before release, not after a business user reports them \nGovernance Guardrails (Protect) \nImplement access controls and row- and column-level policy in the semantic and MCP layers so agents only return data the requesting user is authorized to see \nDefine which agent actions are read-only versus write-capable, and review write paths against REP’s connector governance framework before exposure \nMaintain auditable lineage from any agent-delivered answer back to source, sufficient for Finance and governance review \nVendor Direction (Own) \nAct as technical owner of our external data engineering partner: write build specs precise enough to execute and measure against \nReview delivered work against acceptance criteria and reject what misses the bar - professionally and with evidence \nKeep vendor scopes bounded to vertical, testable work so REP is never dependent on a vendor to understand its own data \nWhat You Will Bring: \nTo perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. \nA hardener, not a gatekeeper: You see a business user’s working prototype as a gift, not a mess. You can read what they built, understand the intent, and harden it without taking ownership away from them. \nA semantic and context engineer: You know when a problem belongs in the semantic layer versus a prompt, application logic, or an ad-hoc query - and you can defend the line. You can tell a definitional problem from a grain problem from a source-system problem. \nVerification-minded: You assume AI-generated output is wrong until tested, and you build the test before trusting the result. You say so plainly when confidence is low. \nAI-native, not AI-dependent: You treat AI coding assistants as a velocity multiplier under review, not an authority. You read and test what they produce before it ships. \nA governance owner: You apply governance to your own work without being asked, make the conservative call on data exposure when scope is ambiguous, and hold the architecture gates under pressure. \nA technical owner of partners: You translate business needs into specs a vendor can execute, and you are comfortable telling a partner their work does not meet the bar. \nEducation and/or Experience: \nBachelor's degree (B. A.) from four-year college or university \n5+ years in analytics engineering, data engineering, or a closely related role, operating as a senior individual contributor \nWritten high-performance SQL and Python dataflows against a cloud warehouse - Microsoft Fabric preferred; Snowflake, Databricks, or BigQuery experience transfers \nBuilt and maintained models in a medallion (bronze/silver/gold) architecture with dbt-style transformation, testing, and documentation discipline \nDesigned or owned a semantic layer or metrics layer that served as the single source of truth for business metrics \nUsed AI coding assistants (e.g., Claude Code, Cursor, Copilot) to materially accelerate data pipeline delivery \nBuilt or contributed to evaluation frameworks for LLM or text-to-SQL accuracy, or exposed governed data to AI agents via MCP or similar interfaces (a strong plus) \nWorked across messy source systems such as NetSuite, Shopify, Amazon SP-API, or HubSpot without having the model handed to you \nImplemented row- and column-level security, access policy, and lineage in a production data platform \nTaken analyst- or business-built prototypes (spreadsheets, notebooks, BI workbooks, or AI-generated reports) and turned them into production data products \nDirected external data engineering partners through specs and acceptance testing \nCore Competencies: \nAction Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm. \nResourcefulness - Securing and deploying resources effectively and efficiently. \nCollaborates - Building partnerships and working collaboratively with others to meet shared objectives. \nSelf-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels. \nSemantic & Context Engineering - Designing metric definitions, join paths, and context that let agents query data deterministically and accurately. \nTechnical Craftsmanship - Writing tes...","description_format":"text","description_chars":16229,"description_truncated":true,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["401k plan","Hybrid work","Parental leave"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Fitness Equipment"],"lifecycle":[{"event":"open","at":"2026-10-28T20:19:44Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":1,"expected_fill_days":53,"reasons":["conf:3","velocity","win:early"],"computed_at":"2026-10-30T05:48:23Z"},"pay":null,"html_url":"https://alion.io/job/rep-fitness-ai-analytics-engineer","json_url":"https://alion.io/job/rep-fitness-ai-analytics-engineer.json","meta":{"generated_at":"2026-10-30T07:36:53Z","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":10680,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-31T00:00:00Z"}},"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":687509},"rest":"https://alion.io/mcp/rest/get_company?id=687509"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Frep-fitness-ai-analytics-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%2Frep-fitness-ai-analytics-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%2Frep-fitness-ai-analytics-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/rep-fitness-ai-analytics-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Frep-fitness-ai-analytics-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3418700}},{"tool":"search_jobs","arguments":{"company":"repfitness.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3418700},"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. 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