Top Analytics Jobs in Ottawa

Maintain and optimize data pipelines and models, monitor and resolve data issues, translate stakeholder needs into SQL, build and maintain internal dashboards, support ad-hoc requests, and apply software engineering best practices under senior guidance.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Perform hands-on product and player analytics across onboarding, retention, monetization, progression and LiveOps. Build and QA ETL pipelines in an AWS analytics stack, write cost- and performance-aware SQL, support A/B testing, and create reusable AI-enabled analytical workflows and notebooks to inform product, marketing, and LiveOps decisions.

Own end-to-end analytics modeling for a business domain: build and maintain dbt models and marts on Snowflake, develop the BI semantic layer, define consistent metrics, create self-serve data products, and partner with Finance, Sales, Marketing, CS, and Data Platform to translate business questions and ensure data quality.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

As a Senior Analytics Engineer, you will build a modeling layer in dbt and Looker, mentor others, and ensure actionable insights are delivered, while migrating from legacy data systems to a modern stack.

Lead analytics strategy, governance, and reporting to create trusted metrics. Drive insights across churn, retention, product telemetry, and marketing. Build analytical models, oversee Looker BI, partner with Data Engineering, establish data quality and AI-readiness, and hire and grow the analytics team to influence leadership decisions and business outcomes.

Partner with product, marketing, and biz-dev to drive data-driven growth. Design, run, and analyze A/B tests; use ML and analytics to inform acquisition and partnership strategies; automate processes to improve operational efficiency.

Own analytics for a core business area (growth, product, AI, or partnerships): define problems, build metrics/reports, run analyses and experiments, surface insights, and drive cross-functional decisions to improve key business metrics end-to-end.

Own the analytics function for an embedded business unit: define problems, build reports and experiments, analyze data, surface insights, and drive cross-functional decisions to improve product, growth, AI, or partnerships.

Support BizOps & Analytics by driving data-driven decisions across growth initiatives: partner with product, marketing, and BD; design and analyze A/B tests; identify scalable acquisition levers; automate manual processes; and leverage advanced ML models to generate actionable insights that shape product and partnership strategy.

Embed with a core business area to define problems, build reports and experiments, analyze data, surface insights, and drive recommendations and execution to improve product, growth, AI, or partnerships.

Partner with product, marketing, and BD to drive data-driven growth. Design, run, and analyze A/B tests, define acquisition and partnership levers, automate processes, and extract insights from advanced ML models to scale profitable user and partner growth.

Partner with cross-functional teams to drive data-driven growth: design and analyze A/B tests, leverage ML models for insights, optimize user acquisition and automate operational processes to scale customer growth and partnerships.

Lead embedded BizOps analytics for a core business area (growth, product, AI, partnerships). Define problems and metrics, pull and analyze data, build reports and experiments, surface insights, make recommendations, and drive decisions and execution end-to-end to improve key business metrics.

Build and scale the finance function: own core accounting, reporting, close processes, internal controls, compliance, and finance systems. Improve AP/AR, treasury, tax, audit readiness, automation, forecasting, and stakeholder reporting while partnering with the CFO and leadership.

Lead revenue operations and business insights by owning GTM and revenue data integrity. Build reporting and AI-driven insights, run billable usage analysis, manage data flows with Fivetran/Salesforce/Sigma, investigate billing disputes, and translate data into actionable narratives for leadership.

The Senior Software Engineer will focus on backend development, designing and maintaining APIs and services, collaborating on features, and enhancing integrations at Motion.

The Finance Director will lead the finance team, overseeing financial planning, analysis, and management practices to support the company's growth and performance optimization.

The Analytics Manager will lead analytics for the Payments and Identity lifecycle, manage a team of analysts, develop KPIs, and ensure data quality while collaborating with cross-functional teams.

Lead design, development, and deployment of GenAI and NLP solutions (RAG, LLM-powered apps, agentic AI, Document Intelligence). Work with clients to translate business needs, build end-to-end ML pipelines, mentor teams, present results to stakeholders, and operationalize scalable, governed AI systems.

Design, build, and maintain scalable dbt transformation pipelines and dimensional data models on Databricks. Partner with data engineering and stakeholders to deliver trusted silver/gold datasets, implement data quality tests and monitoring, enable self-service semantic layers, and enhance LLM-driven tooling to accelerate analytics and automation.

Own cloud operational health, architecture intelligence, and cost optimization. Analyze cloud spend, build dashboards/reporting, enforce governance, review architecture for cost impact, and translate technical findings for engineering, finance, and executives.