Senior Analytics Engineer - CANADA (Remote)

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

• Own and evolve our dbt project — ensuring models are performant, well-tested, and documented.

• Design and maintain the Snowflake data warehouse and ingestion processes.

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

• Build and maintain custom Python/Airflow pipelines to ingest data from third-party APIs into Snowflake.

• Design and operate cross-system reconciliation models that compare data across source systems to surface discrepancies and protect revenue.

Drive Data Quality & Automation

• Implement testing and observability for analytics pipelines.

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

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

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

Cross-Functional Collaboration

• Act as data liaison between Engineering, GTM, and Finance — ensuring consistent metric definitions and proper system instrumentation.

• Enable stakeholder self-service access to trusted insights.

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

Build AI-Ready Data Infrastructure

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

• Partner with AI/product teams to scope, build, and validate the semantic layer 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 SaaS environment.

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

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

• Experience modeling Salesforce data — opportunities, contracts, subscriptions, cases, and field history.

• Proven experience building custom ELT pipelines that ingest data from third-party APIs into a cloud data warehouse.

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

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

• 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.

• Experience designing and maintaining semantic layers that serve as governed data interfaces (dbt Semantic Layer, Snowflake Cortex, or similar).

• Comfortable with large-scale data systems (Snowflake, BigQuery, Redshift).

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

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

• Demonstrated success using analytics to drive decisions in a technical or product-focused environment.

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

Nice to Have:

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

• Strong foundation in statistics and experiment design — A/B testing, significance testing, and measuring incremental impact.

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

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

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

What Success Looks Like

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

• Improve data quality and reliability, with clear SLAs and observability around our most critical models.

• Drive down time-to-insight by enabling self-serve access to high-quality datasets and metrics.

• Extreme ownership over critical infrastructure and data models that directly impact product decisions and business growth.

• 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.

• Proactively identify and quantify data discrepancies across systems and drive them to resolution with operational teams.

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