Top Data Jobs in Toronto

Leads strategic pricing and monetization across sales-driven channels, managing pricing partners and advising on complex commercial deals. Develops pricing frameworks, approval processes, deal governance, and scalable tools using automation, AI, and data. Partners with Sales, Product, Engineering, Legal, Risk, and Revenue Operations to improve deal economics, profitability, pricing performance, packaging, and portfolio-level monetization strategy.

Manages adoption, localization, and scaling of enterprise data products for Takeda Canada. Leads solution-agnostic requirements discovery, business analysis, process and data mapping, and root-cause assessment. Translates analytics outputs into actionable business insights, develops Power BI dashboards, supports data quality and governance, and partners with global delivery, engineering, data science, and commercial stakeholders. Drives product adoption, backlog prioritization, stakeholder alignment, and continuous improvement across pharmaceutical commercial data initiatives.

Owns Takeda Canada's data roadmap, governance, stewardship, quality monitoring, documentation, and integration into enterprise data platforms and customer data systems. The role leads master data management, data quality remediation, standards alignment, source onboarding, consent controls, and data flow validation. It partners with business, technology, vendors, and delivery teams to enable analytics and business use cases, while improving data fluency through training, playbooks, and stakeholder support.

Leads enterprise data quality and governance initiatives, including data profiling, validation, cleansing, monitoring, metadata management, cataloging, lineage, classification, and data protection. Partners with business stakeholders, data owners, engineering, architecture, analytics, and security teams to define quality standards, remediate issues, improve governance-platform adoption, and establish data sensitivity controls. Provides technical leadership across global data environments and supports AI-enabled classification and responsible data use.

Own the market data product strategy supporting front-office trading and EMS capabilities. Consolidate data providers, reference data, gateways, and related services while defining ontology, standards, and common data models. Manage requirements, prioritize initiatives with business, architecture, and technology stakeholders, and help deliver the target-state market data platform and roadmap. The role requires strong financial-services market data expertise and experience with OMS, EMS, and trading technology.

Own and evolve the security master across multiple asset classes, ensuring reference-data quality, governance, controls, reconciliation, and integrity. Lead vendor data sourcing, identifier cross-referencing, corporate actions, exception management, and new instrument onboarding. Partner with technology, operations, compliance, and business teams on data models, EDM platforms, regulatory requests, and reference-data capabilities. Requires strong capital-markets reference-data expertise, SQL, and experience with EDM platforms and data-governance frameworks.

Design and build a scalable capital markets transaction data warehouse and analytics platform. Integrate trade data with Security Master, Reference Data, and Market Data; develop data models covering instruments, securities, issuers, pricing, and accounts; and establish ingestion, enrichment, reconciliation, validation, lineage, and exception-management controls. Partner with Trading, Operations, Analytics, Product, and Engineering teams to deliver trusted, high-volume, analytics-ready financial data solutions.

Lead Data Scientist analyzing global fraud data to identify patterns, develop predictive models, and prevent payment fraud. The role builds fraud-related products and services, extracts insights from large datasets, collaborates with stakeholders, and presents findings to management and clients. Required skills include Python, Spark, complex data queries, and advanced data analysis. Experience with agentic AI systems and Unix is preferred, along with payments or fraud expertise.

Supports sales, customer success, and product development for Mastercard Identity through data analysis, machine learning, rules-based fraud models, and proof-of-value testing. Advises customers on fraud workflows, analyzes data features, supports API integration QA, and provides product recommendations. Builds customer relationships, translates business needs into technical solutions, contributes to product specifications and training, and serves as a subject matter expert on identity data and products.

Build and scale backend-focused software for Braze’s petabyte-scale Data Lakehouse. Develop data pipelines, asynchronous processing, ETL applications, and end-to-end features across platforms such as Snowflake, ClickHouse, and Databricks. Drive architectural decisions, contribute to technical strategy, and deliver scalable web applications handling high volumes of data and traffic in an agile environment.

Build and maintain backend-focused, end-to-end software for Braze’s petabyte-scale Data Lakehouse platform. Develop infrastructure and tools supporting Snowflake, ClickHouse, and Databricks, manage high-volume event data, and deliver front-end experiences when needed. Collaborate across teams, apply strong software design and algorithms knowledge, and help scale systems handling billions of daily rows and significant customer and internal workloads.

Leads sales professionals and manages major corporate accounts for HPE’s data services and storage portfolio. Builds customer relationships, understands business and IT priorities, presents relevant services, identifies competitive opportunities, closes new business, expands existing accounts, and achieves revenue, margin, and sales goals, particularly within Ontario government and healthcare organizations.

Build and scale reliable Snowflake data pipelines, ML pipelines, monitoring systems, and AI-driven agentic tooling that automates pipeline generation, testing, and maintenance. The role partners with analysts, data scientists, product teams, and analytics engineers to deliver scalable end-to-end data infrastructure for Babylist’s e-commerce platform.

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.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Provides Canadian privacy, AI, data protection, consumer protection, and regulatory legal advice for Mastercard products and services. Serves as Canadian Data Protection Officer, advises on product launches, privacy-by-design, data use, consent, cross-border transfers, impact assessments, contracts, regulatory readiness, and remediation. Monitors Canadian, U.S., and global developments and translates requirements into practical business guidance, controls, training, and stakeholder communications.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.

Lead data modeling and ownership of critical pipelines supporting Block’s product data foundation. Build reliable, governed, and monitored datasets, data quality and lineage systems, experimentation infrastructure, and AI-assisted automation. Partner with product and engineering teams to translate business needs into end-to-end data solutions, participate in on-call support, and maintain pipeline SLAs.

Design complex data and workflow experiences for Braze’s enterprise customer engagement platform. Conduct user research, create high-fidelity prototypes, refine the design system, and collaborate with product, engineering, research, and analytics teams. Influence product roadmaps and deliver intuitive tools for data modeling, ingestion, integrations, and campaign execution. The role requires systems thinking, strong interaction and visual design, technical collaboration, and evidence-based decision-making.

Build and optimize data models, pipelines, monitoring, lineage, and data quality systems supporting Block’s product data foundation. Own critical data engineering solutions across their full lifecycle, ensure pipeline reliability through on-call support, and translate business and product needs into automated data solutions. Develop AI-assisted workflows and agents for data quality, issue resolution, and operational efficiency while partnering with engineering, product, data science, and machine learning teams.

Partner with sales teams to win strategic data security opportunities. Deliver technical presentations, gather customer requirements, lead product evaluations, configure solutions, and design test plans. The role requires extensive pre-sales experience, security and networking expertise, data protection knowledge, AI fluency, and collaboration across matrixed go-to-market and partner teams.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Lead digital transformation initiatives, enhance commercial strategy, manage technology portfolios, and drive operational excellence in marketing and sales.

Develops, deploys, and monitors production data and machine learning pipelines. Responsibilities include data preparation, exploratory analysis, predictive modeling, feature and algorithm selection, model deployment, infrastructure development, and monitoring model performance, data quality, drift, latency, and reliability. The role also requires communicating insights, collaborating cross-functionally, and translating business problems into scalable data and ML solutions.