Staff Analytics Engineer

Jane

1d•CA$152,000 - CA$237,500•Remote

About The Position

This role is meaningful because you'll be one of the people shaping how Jane accesses and trusts its own data. You'll define the standards and governance that keep our systems reliable as we scale, and help evolve our architecture toward a more self-service, AI-augmented approach. If you like building the foundation other people rely on without always seeing it, this is a great place to do that work.

Requirements

6+ years of professional experience in analytics engineering or a similarly technical data analytics role, preferably within a SaaS or high-tech environment.
Expert-level SQL proficiency and deep data modeling experience, with a proven ability to write performant queries and understand the architectural trade-offs behind database design decisions.
6+ years of hands-on experience building, deploying, and maintaining modular data pipelines using dbt in a production environment, with real comfort in production git workflows (branching, merging, pull request reviews) and automated data quality testing.
Proven experience designing schemas and optimizing query performance on modern cloud data platforms such as Snowflake, Redshift, or BigQuery.
Experience implementing data observability platforms, plus experience using AI to build and improve dbt workflows.
Experience using AI tools to improve the analytics engineering development process, including accelerating data modeling, testing, documentation, and code development.
Experience designing semantic models and enabling self-service analytics through governed datasets, reusable business logic, and trusted metric definitions.

Nice To Haves

If you don’t meet every single qualification but are excited about this role, we’d still love to hear from you.

Responsibilities

Build and operate the core BI platform that powers reporting, analytics, self-service, and AI across Jane.
Own data ingestion, integration, and platform architecture, making thoughtful trade-offs as our systems evolve.
Build and maintain enterprise data models, reusable business logic, and semantic layer definitions that serve as the foundation for trusted analytics.
Establish standards for data quality, testing, governance, observability, documentation, and CI/CD across the analytics platform.
Monitor the health of the analytics platform, identify pipeline failures and recurring issues, and implement scalable, long-term solutions to improve reliability.
Review and certify data pipelines, data models, semantic layer definitions, and data products before they are consumed by the business.
Mentor Analytics Engineers through code reviews, design reviews, and engineering best practices.