AI Analytics Engineer

REP Fitness•Westminster, CO

1d•Hybrid

About The Position

REP is hiring a senior individual contributor to build and govern the data foundation used by its analytics tools and AI agents. The role develops pipelines and gold data products in Microsoft Fabric, while creating certified semantic models and context layers that help people and AI agents return accurate, grounded answers. It owns REP’s metric definitions of record and ensures data and semantics are accessible through Claude, ChatDash, REP MCP, and other development approaches. The engineer will harden analytics prototypes already built and used by business teams, preserving their intended questions and outputs while replacing ad hoc extracts and queries with governed pipelines, clear definitions, tests, and guardrails. The role also establishes evaluation practices for AI answer accuracy, governs agent access and actions, maintains auditable lineage, and serves as the technical owner of an external data engineering partner. It works alongside the AI Solutions Engineer, who builds agents and tools, while this role supplies the governed data they rely on. This position is based at REP’s headquarters in a hybrid work environment, with at least three days in the office and flexibility in which days and hours. The headquarters has a shared workspace, Rocky Mountain views, and a kitchen stocked with snacks, protein powder, and coffee.

Requirements

Bachelor’s degree from a four-year college or university.
At least 5 years of experience in analytics engineering, data engineering, or a closely related role, operating as a senior individual contributor.
Experience writing high-performance SQL and Python dataflows against a cloud warehouse.
Experience building and maintaining models in a medallion architecture with dbt-style transformation, testing, and documentation discipline.
Experience designing or owning a semantic or metrics layer as the single source of truth for business metrics.
Experience using AI coding assistants to materially accelerate data pipeline delivery, while reviewing and testing their output.
Experience working with messy source systems such as NetSuite, Shopify, Amazon SP-API, or HubSpot without a pre-defined model.
Experience implementing row- and column-level security, access policies, and lineage in a production data platform.
Experience converting analyst- or business-built prototypes into production data products.
Experience directing external data engineering partners through specifications and acceptance testing.
Ability to understand and harden business users’ working prototypes without taking ownership away from the builders.
Ability to distinguish whether a problem belongs in the semantic layer, a prompt, application logic, an ad hoc query, metric definitions, data grain, or a source system.
Verification-minded approach: test AI-generated outputs, build tests before trusting results, and communicate uncertainty plainly.
Ability to apply governance independently and make conservative decisions about ambiguous data exposure.
Ability to translate business needs into executable vendor specifications and tell partners when delivered work does not meet requirements.

Nice To Haves

Microsoft Fabric experience is preferred; Snowflake, Databricks, or BigQuery experience is transferable.
Experience building or contributing to evaluation frameworks for LLM or text-to-SQL accuracy, or exposing governed data to AI agents through MCP or similar interfaces, is a strong plus.

Responsibilities

Reverse-engineer business-built Claude and ChatDash solutions to understand their questions, logic, sources, and implied metric definitions.
Replace ad hoc extracts, uploads, and one-off queries with governed Fabric pipelines and certified semantics while preserving existing business workflows.
Make certified data and semantics accessible through Claude, ChatDash, REP MCP, and other development approaches.
Partner with business builders to confirm hardened versions answer the same questions as the original solutions and explain differences.
Build end-to-end Fabric pipelines and gold data products using a medallion architecture, from bronze ingestion through silver cleansing and conformance to gold tables and views.
Generate test suites and automate reconciliation against source-system reports to reduce manual user acceptance testing.
Define specifications, acceptance criteria, freshness targets, and business ownership based on working business versions before productionizing data products.
Keep transformation code version-controlled, tested, and documented so other engineers or agents can extend it.
Deliver a certified semantic layer over Fabric’s gold layer, including metrics, entity definitions, join paths, and grain rules; begin with inventory and net sales.
Establish a single definition of record for each metric and retire or map conflicting definitions across Power BI, Polar, and NetSuite.
Structure business rules, metadata, and documentation as durable working context for agents.
Resolve data-trust issues at their root in definitions and lineage rather than relying on one-off reconciliations.
Create an evaluation framework with known-correct questions and answers and automated regression checks for semantic-layer or model changes.
Publish regular accuracy scorecards for leaders and the AI Governance committee, and catch answer-quality regressions before release.
Implement access controls and row- and column-level policies in semantic and MCP layers so agents return only authorized data.
Define which agent actions are read-only or write-capable and review write paths against REP’s connector governance framework.
Maintain auditable lineage from agent-delivered answers to source data for Finance and governance review.
Act as technical owner of the external data engineering partner by writing build specifications, reviewing work against acceptance criteria, and rejecting work that does not meet requirements.
Keep vendor scopes bounded to vertical, testable work so REP retains understanding of its data.

Benefits

Medical, dental, and vision insurance.
Health Savings Account with employer contributions.
Flexible Spending Account options.
Long- and short-term disability insurance, life insurance, and AD&D insurance, with premiums covered by REP.
401(k) and Roth options with employer match up to 4%.
Employee Assistance Program.
Four weeks of PTO accrued during the first year.
48 hours of sick leave annually.
Seven paid company holidays and one additional floating holiday.
Paid parental leave beginning on day one.
Paid birthday off.
Bonus structure rewarding individual and company-wide performance.
Full access to the employee gym.
Employee discount on REP equipment, apparel, and nutrition.
Snacks and drinks provided for onsite employees.
Professional growth and development opportunities.
Company and team-building events.