Data Analyst

Canals is a bootstrapped, profitable startup transforming logistics and distribution with AI. Our platform seamlessly integrates with the systems distributors, manufacturers, and vendors already use, automating tedious tasks and reducing failure points in moving physical goods across the globe. We’re a 60-person, fully remote team (40 in engineering) across North and South America, operating at millions in ARR and growing rapidly without outside funding.

We are seeking a Data Analyst to join our team, focusing on a platform-first approach rather than simple one-team reporting. In this role, you will be responsible for transforming ad-hoc business inquiries into production-grade data models. These foundational data assets will be critical resources relied upon by the entire company.

While you will collaborate closely with business stakeholders to ensure successful outcomes, your core ownership lies with the data warehouse: ensuring data correctness, observability, and reusability for all.

• Own core business metrics end-to-end: design, implement, test, document, and maintain them in dbt and Snowflake.

• Convert ad-hoc analyses into durable, reusable warehouse artifacts rather than dashboard patches.

• Implement and maintain automated data tests, monitors, and lineage; integrate them into CI.

• Troubleshoot ingestion and transformation issues and ship fixes upstream (dbt/Snowflake) instead of working around problems at the edge.

• Partner closely with other teams to ensure data is performant, stable, and production-ready for models.

• Support product and commercial teams with analysis and insights.

• Improve self-serve documentation, tools, and onboarding so others can rely on the warehouse without manual hand-holding.

• Surface platform roadmap work: identify recurring needs that should become new marts, schemas, or pipelines.

• Production SQL experience (Snowflake, BigQuery, Redshift or similar).

• Solid dbt experience: models, tests, macros, CI practices, and modular modeling patterns.

• Proven track record of converting one-off analyses into reusable data models.

• Comfortable with Python for data tasks, Git-based workflows, and CI pipelines.

• Strong understanding of data lineage, monitoring, and testing best practices.

• Excellent stakeholder communication, able to map business questions to repeatable data products and document assumptions.

• Experience working in fast-paced teams and owning projects end-to-end.