Analytics Engineer, Data

Narvar•Toronto, ON

14h•CA$131,000 - CA$163,000•Remote

About The Position

Narvar is seeking an Analytics Engineer, Data to manage internal analytics enablement, acting as a liaison between business teams and data. This role involves translating stakeholder questions into defined metrics, building them into the company's semantic layer for consistent reuse, and expanding AI-powered analytics. The ideal candidate is comfortable with both stakeholder interactions and data modeling, aiming to increase self-serve capabilities. A key aspect of this role is working with AI tools like Claude in daily analytics workflows, making data more accessible and reliable for them. The position focuses on building and enhancing the semantic layer, including metrics, models, and metadata, to empower teams and AI to answer questions independently. Success is measured by the creation of self-serve analytics infrastructure. The role requires a proactive approach to data management, ensuring data is usable and trustworthy for both internal teams and AI querying surfaces.

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

Nice To Haves

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 recurring operating cadences
You’ve worked across BigQuery, dbt or Cube, Looker, and Airflow / Composer

Responsibilities

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