Analytics Engineer, Narvar

• 3+ years in analytics engineering, data, or a closely related role, including ownership of metrics or data models that other teams rely on
• Deep SQL and hands-on data modeling — dimensional modeling, incremental transformations, and a feel for clean, maintainable models
• Proven experience building and expanding a semantic / metrics layer — its models, definitions, and context — that other teams adopt; you’ve owned what others depend on rather than consumed it
• Extensive hands-on experience using Claude/Codex for analytics — you’ve done substantive analytical work with it and know how to structure data, metrics, and metadata so it answers reliably
• The ability to stand up a new data source end to end — comfort with orchestration, APIs, and batch ETL, not just querying what already exists
• Excellent stakeholder communication — you can lead a conversation with a non-technical partner, walk away with a data spec, and explain a metric so people trust it
• A builder’s mindset — you’re motivated by creating durable, reusable metrics and self-serve infrastructure that scales beyond any single request
• Working knowledge of a cloud data warehouse (GCP / BigQuery preferred), a BI tool such as Looker, and Python for pipeline and tooling work

Desirable

• We’re looking for an Analytics Engineer, Data to own internal analytics enablement — the bridge between our business teams and our data
• You’ll translate questions from product, go-to-market, and executive partners into trusted metrics and build them into our semantic layer so they’re defined once and reused everywhere
• At its core, this is a building role: you’ll expand our semantic layer and the Claude-powered agentic analytics on top of it — the metrics, models, and metadata that let our teams and Claude answer questions directly — so the organization can increasingly answer its own questions
• It suits someone equally at home in a stakeholder conversation and in a data model, who measures success by how much self-serve they create
• We work in an AI-native way: Claude is part of our daily analytics workflow, and much of this role is making our data ever more usable by it
• Partner with product, go-to-market, and executive stakeholders — running discovery on ambiguous questions and scoping the metrics and data they actually need
• Raise data trust — adding the validation, definitions, and documentation that let users rely on the numbers and our tooling
• Expand and own our semantic / metrics layer — defining and maintaining metric definitions and models so analytics are consistent, trustworthy and reusable across the company
• Deliver self-serve and AI-accessible analytics — curated datasets, metrics, and reporting that internal partners and our agentic / LLM querying surface can answer on their own
• Ingest net new data — designing and building pipelines to bring in new sources such as GTM and product-usage data and modeling them for analytics