Analytics Engineer III, Assurance in Toronto

The Analytics Engineer III on the ACV Assurance team is a skilled practitioner who transforms raw data into trusted, decision-ready models and reports that drive the business forward. Sitting at the intersection of data engineering and business intelligence, this role contributes across the full analytics stack - from dbt model design and data quality to Omni BI dashboards - and partners with the Assurance Business to surface insights on arbitration risk and product initiatives. A key objective of this role is reducing ad-hoc analytical bottlenecks over time. You will be expected to answer urgent business questions quickly and directly, while systematically building the underlying dbt models, metric definitions, and BI layer in a way that enables self-serve analytics - including AI-assisted querying - so that business stakeholders can answer common questions themselves.

Analytics Modeling & Data Quality

• Design, build, and maintain dbt models (staging, intermediate, production layers) that serve as the single source of truth for Assurance KPIs, with machine-readability in mind.
• Enforce data quality through dbt tests, source freshness checks, and documentation so downstream consumers can trust what they see.
• Write complex SQL transformations on large datasets; optimize for cost and performance.

Reporting & BI

• Translate business questions into well-scoped analytical requirements; define metrics in collaboration with Assurance stakeholders and keep definitions governed in our semantic layer.
• Build upon our Omni BI semantic layer, enabling self-serve chat and dealer-facing embedded reporting.
• Balance responsiveness to ad-hoc requests while optimizing via building: triage what should be answered once vs. what should be codified so stakeholders or AI tools can self-serve it in the future.
• Deliver clear, compelling data narratives to non-technical stakeholders; support follow-on questions and iterate quickly.

Assurance Business Domains

• Risk: support the business in developing data, predictive, and forecasting models that identify high-risk dealer and vehicle populations to support program management strategy.
• Product: develop dealer-facing dashboards that illustrate their arbitration trends over time.

Project Ownership & Stakeholder Partnership

• Execute scoped projects with general guidance: define approach and timelines with input from a manager or senior team member, and drive day-to-day execution independently.
• Deliver data-driven recommendations.
• Navigate competing priorities across multiple stakeholder groups; propose win-win solutions when technical requirements conflict.

What you will need:

Education

• BA/BS in Statistics, Mathematics, Computer Science, Operations Research, or related.
• Master's or Ph.D. a plus, but offset by demonstrated experience and a deep toolbox.

Experience

• 3+ years of professional experience in analytics engineering, data engineering, or BI.
• Hands-on production experience with dbt (model design, testing, documentation, incremental strategies).
• Expert-level SQL; comfortable with window functions, complex joins, and query optimization in BigQuery or a comparable cloud warehouse.
• Experience delivering analytical initiatives independently, from scoping through stakeholder presentation.
• Experience with Git-based version control workflows.

Soft Skills

• Excellent communicator with a strong attention to detail; genuinely curious about the business and willing to dig into the specifics to get things right.
• Collaborative, low-ego, and invested in the team's collective output.
• Strong instinct for knowing when to answer quickly vs. when to build properly.
• Communicates analytical findings clearly to non-technical audiences.
• Comfortable navigating ambiguity.

Nice-to-Haves

• Proficiency building semantic layers; Omni or Looker BI experience preferred, but similar experience considered.
• Experience with Google Cloud Platform.
• Familiarity with AI-assisted analytics or developer workflows.
• Experience with binary classification and/or loss forecasting.