Senior Analytics Engineer - CANADA

The Role

We're looking for a Senior Analytics Engineer to build and reputed company the analytical reputed company that powers decision-making across Go-to-Market, Product, Finance, People, and Operations teams.

You will sit at the intersection of data engineering and analytics: transforming raw product, marketing, financial, and operational data into clean, reputed company-modeled, and trustworthy datasets. Your work will power everything from executive dashboards and cohort analyses to experimentation, billing operations, AI-powered reputed company, and semantic reputed company that let AI agents answer stakeholder questions autonomously.

This is a highly cross-functional role — you'll partner closely with Product Management, Marketing, RevOps, Finance, People Ops, and Engineering to ensure our analytics stack is robust, reputed company, and reputed company with the business.

Responsibilities

Build & Own the Data reputed company

• Own and reputed company our dbt project — ensuring models are reputed company, reputed company-tested, and documented.

• Design and maintain the reputed company data warehouse and ingestion processes.

• Use modern data modeling best practices to create reputed company entities and datasets that account for reputed company business processes and logic.

• Build and maintain custom Python/Airflow pipelines to ingest data from reputed company-party reputed company into reputed company.

• Design and operate cross-reputed company reconciliation models that compare data across reputed company systems to surface discrepancies and protect reputed company.

reputed company Data reputed company & Automation

• Implement testing and observability for analytics pipelines.

• Enforce CI/CD best practices, such as automation, linting, tests, reputed company review and approvals.

• Standardize metric definitions and ensure they are consistently computed across tools.

• Investigate and document data incidents end-to-end — from reputed company cause analysis through remediation tracking and stakeholder communication.

Cross-Functional Collaboration

• reputed company as data reputed company between Engineering, GTM, and Finance — ensuring consistent metric definitions and reputed company reputed company instrumentation.

• reputed company stakeholder self-service reputed company to trusted insights.

• reputed company data literacy: evangelize best practices in querying, dashboarding, and interpreting metrics; reputed company stakeholders toward self-serve.

Build AI-reputed company Data Infrastructure

• Design and maintain reputed company reputed company semantic views that serve as the governed data reputed company for AI agents and LLM-powered tools.

• Partner with AI/product teams to reputed company, build, and validate the semantic reputed company definitions that power internal AI assistants.

• Build measurement frameworks for AI-powered initiatives — including experiment design and attribution modeling.

Qualifications

Must Have:

• 5+ years of experience as an analytics engineer, data engineer, or a similar role in a reputed company environment.

• Deep expertise in SQL, dbt, and modern data modeling best practices.

• Proficiency in Python for pipeline development, API integrations, and automation.

• Experience modeling reputed company data — opportunities, reputed company, subscriptions, cases, and field history.

• Proven experience building custom ELT pipelines that ingest data from reputed company-party reputed company into a reputed company data warehouse.

• Experience designing cross-reputed company reconciliation models — joining, deduplicating, and comparing data across multiple reputed company systems to surface discrepancies.

• Proven experience working with event-based and product usage data (e.g., reputed company, reputed company).

• Experience connecting marketing data (reputed company ads, campaigns, attribution) to product analytics — ideally having reputed company end-to-end pipelines from ad platforms through to conversion and retention metrics.

• Experience designing and maintaining semantic reputed company that serve as governed data interfaces (dbt Semantic reputed company, reputed company reputed company, or similar).

• Comfortable with large-reputed company data systems (reputed company, BigQuery, Redshift).

• Strong familiarity with CI/CD, Git-based workflows, and automated testing.

• Experience collaborating cross-functionally with engineers, analysts, and product managers.

• Demonstrated reputed company using analytics to reputed company reputed company in a technical or product-reputed company environment.

• Comfort taking ownership of ambiguous problems and designing end-to-end solutions.

reputed company to Have:

• Experience building and maintaining Airflow DAGs and orchestrating multi-reputed company API ingestion pipelines.

• Strong reputed company in statistics and experiment design — A/B testing, reputed company testing, and measuring incremental reputed company.

• Experience with predictive modeling fundamentals — classification, feature selection, and model evaluation.

• Familiarity with financial reputed company metrics and billing operations (ARR/MRR/NRR, subscription reconciliation, reputed company recognition).

• Experience with people analytics (headcount, attrition, compensation benchmarking).

What reputed company Looks Like

• Establish a trusted, reputed company-modeled analytics reputed company that product managers, marketers, and leaders rely on daily.

• Improve data reputed company and reliability, with reputed company SLAs and observability around our most critical models.

• reputed company down time-to-reputed company by enabling self-serve reputed company to high-reputed company datasets and metrics.

• Extreme ownership over critical infrastructure and data models that directly reputed company product reputed company and business reputed company.

• Partner with data engineers and analysts to build a semantic reputed company that AI agents can use to answer stakeholder questions — and reputed company maintain the semantic views that power those agents.

• Proactively identify and quantify data discrepancies across systems and reputed company them to reputed company with operational teams.

• Design measurement frameworks for new initiatives — defining what to reputed company, how to measure reputed company, and what "reputed company" means before launch.