Senior Analytics Engineer - CANADA (Remote)
- Remote
- All data & analytics jobs
- Canada
- Full-time
- Engineering
About the role
The Role
We're looking for a Senior Analytics Engineer to build and scale the analytical foundation 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, well-modeled, and trustworthy datasets. Your work will power everything from executive dashboards and cohort analyses to experimentation, billing operations, AI-powered outreach, and semantic layers 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, scalable, and aligned with the business.
Responsibilities
Build & Own the Data Foundation
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Own and evolve our dbt project — ensuring models are performant, well-tested, and documented.
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Design and maintain the Snowflake data warehouse and ingestion processes.
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Use modern data modeling best practices to create core entities and datasets that account for complex business processes and logic.
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Build and maintain custom Python/Airflow pipelines to ingest data from third-party APIs into Snowflake.
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Design and operate cross-system reconciliation models that compare data across source systems to surface discrepancies and protect revenue.
Drive Data Quality & Automation
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Implement testing and observability for analytics pipelines.
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Enforce CI/CD best practices, such as automation, linting, tests, code review and approvals.
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Standardize metric definitions and ensure they are consistently computed across tools.
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Investigate and document data incidents end-to-end — from root cause analysis through remediation tracking and stakeholder communication.
Cross-Functional Collaboration
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Act as data liaison between Engineering, GTM, and Finance — ensuring consistent metric definitions and proper system instrumentation.
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Enable stakeholder self-service access to trusted insights.
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Drive data literacy: evangelize best practices in querying, dashboarding, and interpreting metrics; coach stakeholders toward self-serve.
Build AI-Ready Data Infrastructure
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Design and maintain Snowflake Cortex semantic views that serve as the governed data interface for AI agents and LLM-powered tools.
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Partner with AI/product teams to scope, build, and validate the semantic layer definitions that power internal AI assistants.
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Build measurement frameworks for AI-powered initiatives — including experiment design and attribution modeling.
Qualifications
Must Have:
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5+ years of experience as an analytics engineer, data engineer, or a similar role in a SaaS environment.
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Deep expertise in SQL, dbt, and modern data modeling best practices.
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Proficiency in Python for pipeline development, API integrations, and automation.
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Experience modeling Salesforce data — opportunities, contracts, subscriptions, cases, and field history.
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Proven experience building custom ELT pipelines that ingest data from third-party APIs into a cloud data warehouse.
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Experience designing cross-system reconciliation models — joining, deduplicating, and comparing data across multiple source systems to surface discrepancies.
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Proven experience working with event-based and product usage data (e.g., Posthog, Mixpanel).
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Experience connecting marketing data (paid ads, campaigns, attribution) to product analytics — ideally having built end-to-end pipelines from ad platforms through to conversion and retention metrics.
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Experience designing and maintaining semantic layers that serve as governed data interfaces (dbt Semantic Layer, Snowflake Cortex, or similar).
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Comfortable with large-scale data systems (Snowflake, BigQuery, Redshift).
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Strong familiarity with CI/CD, Git-based workflows, and automated testing.
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Experience collaborating cross-functionally with engineers, analysts, and product managers.
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Demonstrated success using analytics to drive decisions in a technical or product-focused environment.
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Comfort taking ownership of ambiguous problems and designing end-to-end solutions.
Nice to Have:
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Experience building and maintaining Airflow DAGs and orchestrating multi-source API ingestion pipelines.
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Strong foundation in statistics and experiment design — A/B testing, significance testing, and measuring incremental impact.
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Experience with predictive modeling fundamentals — classification, feature selection, and model evaluation.
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Familiarity with financial SaaS metrics and billing operations (ARR/MRR/NRR, subscription reconciliation, revenue recognition).
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Experience with people analytics (headcount, attrition, compensation benchmarking).
What Success Looks Like
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Establish a trusted, well-modeled analytics layer that product managers, marketers, and leaders rely on daily.
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Improve data quality and reliability, with clear SLAs and observability around our most critical models.
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Drive down time-to-insight by enabling self-serve access to high-quality datasets and metrics.
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Extreme ownership over critical infrastructure and data models that directly impact product decisions and business growth.
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Partner with data engineers and analysts to build a semantic layer that AI agents can use to answer stakeholder questions — and actively maintain the semantic views that power those agents.
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Proactively identify and quantify data discrepancies across systems and drive them to resolution with operational teams.
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Design measurement frameworks for new initiatives — defining what to track, how to measure impact, and what "success" means before launch.
