Senior Analytics Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Analytics Engineer based in Canada.

This is an opportunity to build and scale the analytical foundation behind critical business and product decisions in a high-growth SaaS <environment.You> will work at the intersection of data engineering, analytics, and business strategy, transforming complex data into trusted, actionable insights.Your work will support teams across Product, Go-to-Market, Finance, People, Marketing, and <Operations.You> will own key components of a modern analytics stack, including dbt, Snowflake, Python, Airflow, data pipelines, and semantic layers.The role also offers the opportunity to build AI-ready data infrastructure that enables AI agents and LLM-powered tools to answer business questions <reliably.You> will have significant ownership over data quality, governance, observability, and measurement frameworks.This is a highly collaborative environment where your technical expertise will directly influence product decisions, operational efficiency, and business growth.

Accountabilities

• Own and continuously evolve the dbt analytics environment, ensuring models are performant, tested, documented, and aligned with modern data modeling practices.
• Design, maintain, and optimize Snowflake data warehouse structures and data ingestion processes.
• Develop core entities and datasets that accurately represent complex business processes, metrics, and operational logic.
• Build and maintain Python and Airflow pipelines for ingesting data from third-party APIs into the cloud data warehouse.
• Design cross-system reconciliation models to identify discrepancies, protect revenue, and improve data consistency across multiple source systems.
• Establish robust testing, observability, CI/CD, linting, code review, and approval practices for analytics pipelines.
• Standardize metric definitions and ensure consistent calculations across dashboards, analytics tools, and business functions.
• Investigate data incidents from root cause through remediation, documentation, and stakeholder communication.
• Partner with Engineering, Product, Marketing, RevOps, Finance, and People teams to align data definitions, instrumentation, and analytical requirements.
• Enable stakeholder self-service by providing trusted datasets, metrics, and guidance on effective querying, dashboarding, and data interpretation.
• Promote data literacy and coach business stakeholders on analytics best practices.
• Design and maintain governed semantic views that provide reliable interfaces between business data and AI agents or LLM-powered applications.
• Collaborate with AI and product teams to define, implement, and validate semantic layers supporting internal AI assistants.
• Develop measurement frameworks for AI-powered initiatives, including experiment design, attribution, and impact measurement.
• Proactively identify data discrepancies, quantify their business impact, and coordinate resolution with operational teams.
• Define measurement approaches for new initiatives, establishing success criteria and tracking requirements before launch.

Requirements

• 5+ years of professional experience as an Analytics Engineer, Data Engineer, or in a similar role, preferably within a SaaS environment.
• Deep expertise in SQL, dbt, and modern data modeling principles.
• Strong Python skills for pipeline development, API integrations, automation, and data processing.
• Experience modeling Salesforce data, including opportunities, contracts, subscriptions, cases, and field history.
• Proven experience developing custom ELT pipelines that ingest third-party API data into cloud data warehouses.
• Experience designing reconciliation models that join, deduplicate, compare, and validate data across multiple source systems.
• Hands-on experience with event-based and product usage data, using tools such as PostHog or Mixpanel.
• Experience connecting marketing data—including paid advertising, campaigns, and attribution—to product analytics and downstream conversion and retention metrics.
• Experience designing and maintaining governed semantic layers, such as dbt Semantic Layer, Snowflake Cortex, or comparable technologies.
• Strong familiarity with large-scale cloud data platforms such as Snowflake, BigQuery, or Redshift.
• Experience with Git-based development workflows, CI/CD, automated testing, and data quality practices.
• Experience collaborating effectively with engineers, analysts, product managers, and business stakeholders.
• Demonstrated ability to use analytics to influence decisions in technical, product, or business environments.
• Strong ownership mindset and the ability to independently navigate ambiguous problems and design end-to-end solutions.
• Experience with Airflow DAGs and multi-source API orchestration is a plus.
• Knowledge of statistics and experimentation, including A/B testing, significance testing, and incremental impact measurement, is desirable.
• Familiarity with predictive modeling concepts such as classification, feature selection, and model evaluation is an advantage.
• Understanding of financial SaaS metrics and billing processes, including ARR, MRR, NRR, subscription reconciliation, and revenue recognition, is a plus.
• Experience with people analytics, including headcount, attrition, and compensation benchmarking, is beneficial.

Benefits

• Competitive compensation and benefits package.
• Opportunity to work in a high-growth SaaS and technology environment.
• Significant ownership over critical analytics infrastructure and data products.
• Opportunity to work with modern data technologies including Snowflake, dbt, Python, Airflow, and AI-enabled semantic layers.
• Exposure to AI-powered analytics and infrastructure supporting LLMs and autonomous data experiences.
• Cross-functional collaboration with Product, Engineering, Marketing, Finance, People, RevOps, and Operations teams.
• Opportunity to influence strategic decisions through trusted data and measurement frameworks.
• Remote-friendly work environment in Canada.
• Inclusive and collaborative culture focused on innovation, ownership, and continuous improvement.
• Equal opportunity workplace committed to considering qualified candidates fairly.