Analytics Engineer, Data

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Narvar

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2012

🛍️ eCommerce

☁️ SaaS

🛒 Retail

💰 $30M Series C on 2018-08

eCommerce • SaaS • Retail

Narvar is a company that specializes in creating intelligent post-purchase experiences for ecommerce businesses. Their platform provides a range of services to enhance customer engagement and retention, from setting clear delivery expectations and proactive multi-channel messaging to seamless returns and exchanges. Narvar's solutions are designed to build loyalty by personalizing the consumer journey and optimizing reverse logistics, ultimately converting and retaining customers while increasing revenue and reducing costs. The company boasts a powerful network and integrations with popular platforms like Shopify, Zendesk, and Salesforce, and supports over 2,000 retailers globally. Narvar's services cater to a variety of industries including apparel, electronics, and beauty, providing exceptional post-purchase experiences through actionable insights and data-driven interactions across their ecosystem.

📋 Description

• 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

🎯 Requirements

• 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.