Top Data Analytics Jobs in Toronto

Develop Python and SQL analytics tools for stress testing, analyze credit losses and capital impacts, apply generative AI and LLMs to risk workflows, and build dashboards and visualizations. Support AI-enabled risk-monitoring prototypes through requirements gathering, testing, documentation, and collaboration with quantitative analysts. Communicate technical findings clearly to technical and non-technical stakeholders while contributing to benchmarking, automation, reporting, and data validation.

Leads budgeting, forecasting, long-range planning, financial reporting, KPI analysis, dashboards, data governance, financial modeling, and scenario analysis. Partners with Finance and business teams to improve forecasting accuracy, automate reporting, resolve data discrepancies, and support strategic initiatives and special projects.

Analyze wholesale credit portfolio data to identify risks and trends, prepare recurring risk reports, conduct sector deep dives, and translate data into recommendations. The intern will support data extraction, transformation, visualization, reporting infrastructure, and analytics design using Excel, SQL, Python, Tableau, and emerging generative AI tools.

Sell new business and manage, grow, and renew a portfolio of clients for CoStar’s data and analytics products. Responsibilities include prospecting, product demonstrations, consultative selling, closing, onboarding, training, account management, and relationship development with commercial real estate professionals. The role also involves attending industry events, representing CoStar, exceeding sales targets, and building expertise in commercial real estate and CoStar’s product suite.

Lead PPC and SEO strategy and performance for multiple clients. Own audits, roadmaps, reporting, QA, and team oversight across paid and organic channels. Support sales, standardize playbooks, and build scalable marketing frameworks and dashboards.

Own and scale client-facing data transformation pipelines for financial and operational data. Build reliable analytical models, data integrity tests, and integrations across diverse sources. Partner with Customer Success and Engineering to troubleshoot data issues, optimize critical pipelines, establish engineering standards, and improve developer experience. Use dbt, SQL, Python, BigQuery, and AI-enabled workflows to deliver performant, well-documented FP&A data products while mentoring teammates and contributing to hiring and onboarding.

Analyze commercial credit risk and portfolio data to identify trends, prepare recurring risk reports, and support ad hoc stakeholder requests. Conduct industry and portfolio deep dives, contribute to data infrastructure and reporting design, and translate data into credit risk recommendations. Use Excel, SQL, Python, and visualization tools across the data lifecycle while developing knowledge of commercial lending, financial statements, macroeconomic trends, and generative AI applications.

Own complex analytics engineering workstreams, design scalable analytical models and semantic layers, establish modeling and quality standards, improve AWS workload performance and reliability, and support AI-driven analytics capabilities. Partner with platform, product analytics, engineering, and governance teams; guide migrations and technical debt reduction; ensure privacy, security, and business fit; and mentor analytics engineers and analysts.

Partner with Sales Strategy, Solution Architects, and Ecosystem teams to translate pre-sales questions into analytics and AI solutions. Build BI and AI tools, maintain analytics foundations (Snowflake/dbt/Tableau), run cohort/segmentation and experimentation, and surface insights to improve trials, adoption, conversion, and partner-influenced wins.

Lead complex full-stack software projects, design scalable architectures, mentor engineers, build end-to-end product features, develop Python and Node.js APIs, work across modern front-end frameworks, troubleshoot defects, and deliver maintainable, tested code in an Agile SaaS environment.

Lead deployment and sustainment of a production operating system across the full biomanufacturing value stream. Perform diagnostics, build tiered daily management, run kaizen events, mentor teams in Lean problem-solving, connect performance to throughput and release readiness, and embed durable standard work and KPI governance.

Serve as a strategic analytics advisor to clients: design and audit Adobe Analytics and CJA implementations, run measurement workshops, build dashboards and reports, translate data into actionable stories, present findings, and enforce information security and data privacy best practices.

Lead development of scalable data products for Revenue: design dbt models, metrics, dashboards, governance, and AI enablement. Drive data architecture, quality, lineage, documentation, and automation; mentor analysts and align stakeholders to deliver reliable revenue analytics and reporting.

The GTM Lead for Insurance will build and manage enterprise insurance accounts, develop relationships with senior industry leaders, and shape sales strategies for AI solutions.

Leads Medavie’s enterprise data, analytics, and AI strategy and program governance. Defines the multi-year roadmap, strategic guardrails, portfolio priorities, governance forums, decision rights, reporting, metrics, and value-realization practices. Partners with executives, business lines, IT, risk, privacy, and legal teams to align investments, manage strategic risks, promote responsible AI, and advance data and AI literacy across the organization.

Build, segment, and activate audiences in Salesforce Data Cloud and Marketing Cloud; support campaign QA and launch readiness; attribute leads by acquisition channel; monitor campaign performance and contribute to reporting and insights; coordinate between marketing, analytics, lifecycle marketing, technology, and finance to improve martech processes and execution.

Serves as a strategic analytics partner to Marketing, owning funnel, attribution, ROI, account scoring, experimentation, and pipeline analytics. Builds scalable datasets, dashboards, and quality controls while connecting customer, campaign, product usage, CRM, and revenue data. Delivers executive-ready reporting, QBR insights, and recommendations that improve conversion, marketing investment effectiveness, account-based marketing, product adoption, and pipeline growth.

As an Associate Product Manager at Switch, you will drive customer success, manage software implementation, and collaborate with engineers while shaping the product roadmap based on customer feedback and analytics.

Lead discovery, prototyping, and commercialization of ML/AI and edge computing solutions. Build hands-on prototypes, collaborate with data scientists and engineers, engage customers and partners for co-innovation, influence product roadmaps, participate in standards/open-source, and evangelize thought leadership to advance productization and market adoption of intelligent networking capabilities.

Lead and scale a global engineering organization to deliver enterprise-grade AI/GenAI SaaS products. Define technology strategy and roadmap, drive architecture for LLMs/RAG/NLP systems, ensure scalability/reliability, establish DevOps and quality practices, partner with Product/Sales/Customer teams, and manage hiring, budgets, and leadership development.

The Analytics Engineer partners with business stakeholders and data teams to translate reporting needs into scalable analytics solutions. Responsibilities include integrating disparate datasets, developing optimized SQL queries and reporting tables, improving data models, building executive dashboards and KPI frameworks, maintaining semantic-layer definitions and governance standards, and applying software engineering practices such as version control, continuous integration, and code reviews. The role also provides actionable business insights and supports evolving analytics requirements.

Build and own scalable data models for PlayStation game analytics, supporting new title launches and efficient, consistent querying. Collaborate with game studios and analysts to design and validate in-game telemetry, translate business needs into data solutions, and expand use of dbt and Airflow. Prioritize performance, consistency, quality, testing, and repeatable database development across analytics platforms.

Lead and grow a Support Engineering team that resolves complex product issues, integrations, and data workflows. Define escalation and triage practices, build scalable diagnostics and internal tools, collaborate closely with Engineering and Product, mentor engineers on debugging, logs, and database querying, and create repeatable processes to improve customer outcomes and support efficiency.

Lead a small data engineering team to deliver trusted marketing data pipelines and integrations, define success metrics, ensure data quality, enable marketing platforms, and align data roadmap with business growth.

Lead analytics strategy for C2C M&A integrations, driving product analytics, experimentation, metric standards, AI-enabled prototypes, and cross-functional data solutions to improve seller outcomes and business performance.