Job Opportunities API

Narvar is growing! We’re building the data infrastructure behind the post-purchase experiences of hundreds of millions of consumers. When a shopper tracks a package, initiates a return, or gets a delivery notification through one of our 1,500+ brand partners, our data systems are doing the work behind the scenes. 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. For this role, you should be located in Canada and able to work within EST/EDT hours. We are fully remote. What You’ll Work On 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 What We’re Looking For We care about judgment and ownership over credentials. 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 Signals That You’ll Thrive Here These aren’t hard requirements, but strong indicators: You’ve designed or expanded a semantic or metrics layer and made it stick across teams You’ve owned self-service analytics and metrics like pipeline, retention, product usage You’ve built agents, Claude skills, or MCP tooling that other people rely on You’ve supported executive reporting and recurri