Top Data Jobs in Toronto

Lead and grow a data engineering team to build and operate ingestion, transformation, orchestration, and serving layers. Drive data quality, observability, cost efficiency, self-serve analytics, engineering best practices, and cross-functional partnerships to deliver reliable, reusable data products for product, GTM, finance, and CTO stakeholders.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

Design, build, and scale Snowflake ingestion pipelines and ML/LLM systems. Create agentic tooling to generate, test, and maintain pipelines. Collaborate with analysts, data scientists, and product teams to deliver reliable, high-performance data infrastructure and operationalize models for an AI-first e-commerce platform.

Lead sourcing strategies and RFx processes for claims, technology, and indirect categories. Manage supplier selection, negotiate contracts, mitigate vendor risk, liaise with legal and stakeholders, deliver executive presentations, track savings and performance, and oversee contract lifecycle and compliance.

Lead end-to-end strategic sourcing for Claims, Technology and Indirect categories: run RFx processes, negotiate contracts, manage vendor relationships, assess and mitigate risk, deliver executive presentations, and drive savings and category strategy while managing multiple stakeholder priorities and contract lifecycles.

Engage with customers to sell Salesforce Customer 360, build relationships, develop account strategies, and drive growth within assigned accounts.

The Data Engineer will develop and maintain data pipelines, ensure data quality, and collaborate with business teams to implement insightful data solutions.

Analyze Talent Acquisition and people data to identify hiring trends, bottlenecks, quality, efficiency, and equity opportunities. Partner with stakeholders to frame ambiguous questions, conduct rigorous analyses, validate data, monitor metrics, and deliver actionable recommendations. Lead analytics projects from requirements gathering through execution and impact measurement, while applying product management and design thinking to improve employee and candidate experiences.

Lead pricing and interchange analytics to define country pricing strategy, run financial simulations, model outcomes, and support account teams. Design strategies, conduct competitive and economic analysis, supervise cost studies, manage SQL ETL and BI processes, and prepare recommendations for management while collaborating with cross-functional stakeholders.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Lead a data science team to design and deliver merchant registry and profiling ML capabilities for onboarding and monitoring. Define strategy, guide model development and validation, promote reusable data assets, partner with Product and Engineering, drive Agile delivery, hire and mentor team members, and communicate impact to senior leadership to scale production ML systems on modern cloud/data platforms.

Lead the design, build, and support of scalable on-premise and Azure cloud data platforms, ETL/ELT, streaming and batch solutions, data lakes/warehouses, APIs and AI-enabled applications. Drive migrations, deployments, automation, performance tuning, and compliance for large-scale fintech data environments while partnering with product, analytics, architecture and engineering teams.

In this role, you will define and execute the go-to-market strategy, drive growth, and lead customer success efforts at Reflow, a workplace intelligence platform.

As a Product Manager at Reflow, you will lead product strategy, execution, and delivery while ensuring customer engagement and managing engineering dependencies in an early-stage startup environment.

The Director of Product Management will lead product strategy across infrastructure, software enablement, and customer-facing platforms, enhancing both technical and user experience while managing a team of product managers.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Use Coursera learner and product data to define and track KPIs, analyze consumer behavior, produce dashboards, partner with business leaders to inform consumer strategy, and present actionable insights to cross-functional teams to drive product and business decisions.

Extract, transform, validate, and load client PDFs and structured data into the MineralSoft platform using AI-driven extraction and ETL tools. Configure datasets, run queries and reports, troubleshoot integrations with EnergyLink, and ensure data quality and consistency while collaborating with cross-functional teams.

Design, build, and scale real-time and batch data infrastructure and pipelines (Kafka, Databricks, Snowflake) to power analytics, reporting, and AI-powered data experiences. Drive data modeling, governance, observability, and best practices while partnering with cross-functional teams and mentoring engineers.

Lead design and rollout of a fit-for-purpose data governance program: manage data lifecycle, implement governance frameworks and tools, drive data catalogs/lineage, upskill stewards, define KPIs, and partner cross-functionally to improve data quality, compliance, and usability.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Design and maintain Azure DevOps CI/CD pipelines and automate deployment of Microsoft Fabric, Azure Data Factory, Synapse, Databricks, and Power BI. Implement IaC (Bicep/ARM), manage Azure environments, security, secrets, monitoring and logging, and collaborate with cross-functional teams to improve automation and platform delivery.

Design, build, and operate production AI systems and marketing data infrastructure. Develop retrieval pipelines, agent orchestration, CDP integrations, ETL/ELT pipelines, and analytics. Automate workflows with AI, ensure data quality and reliability, partner with stakeholders, ship Python and SQL code, mentor engineers, and drive requirements to production.