Top Data Jobs in Toronto

Supports AML/ATF regulatory reporting by entering, validating, submitting, and documenting Suspicious Transaction Reports and other FINTRAC filings. Tracks deadlines and filing status, maintains accurate records, collaborates with AML analysts and investigators, escalates data-quality issues, and supports testing, process documentation, and continuous improvement for reporting systems.

As Senior Product Manager, you'll manage the product roadmap, engaging with healthcare professionals and driving cross-functional execution in a high-growth tech environment.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Supports the end-to-end business intelligence and reporting lifecycle within the Third-Party Governance Office. Sources, transforms, and analyzes data using Databricks and related tools, then creates dashboards and reports in Power BI. Partners with stakeholders to define requirements, deliver risk and operational insights, improve data quality, and automate reporting workflows with Power Automate. Applies data visualization, storytelling, and continuous improvement practices.

Design, build, and maintain Databricks/Spark ETL pipelines and Bronze-Silver-Gold architectures; implement event-driven streaming with Pub/Sub and Protocol Buffers; prepare datasets for LLM fine-tuning and embeddings; integrate third-party sources (Shopify, Klaviyo); implement attribution, CLV, and analytics models; ensure data quality, monitoring, and idempotent retry-safe pipelines.

Lead and develop a team of data engineers building scalable real-time and batch pipelines and cloud data platforms. Set technical direction, manage major initiatives, and establish practices for architecture, testing, observability, reliability, and operational excellence. Partner with Product, Data Science, Analytics, and Engineering teams to deliver trusted data solutions supporting analytics, experimentation, machine learning, personalization, and high-volume marketplace experiences.

Design, build, and operate scalable real-time and batch data pipelines using Kafka, Spark, Flink, Airflow, Databricks, AWS, and dbt. Implement data quality frameworks, optimize distributed workloads, and develop reliable cloud-native data platforms. Own initiatives from architecture through production support while collaborating with Data Science, Product, Analytics, and Engineering teams. Create reusable engineering patterns, improve system scalability and performance, and maintain documentation for critical data systems.

Design, develop, and own scalable real-time and batch data pipelines using Kafka, Spark, Flink, Airflow, Databricks, and AWS. Implement data quality frameworks, oversee projects from design through long-term support, identify scalability risks, document systems, and collaborate with data science, product, and engineering teams to guide technical roadmaps.

Leads end-to-end data science and AI initiatives, from use-case discovery and data assessment through model development, validation, deployment support, monitoring, and optimization. Partners with product owners, engineers, technology teams, and control functions to deliver scalable solutions. Uses Python, PySpark, SQL, statistics, machine learning, NLP, experimentation, visualization, and cloud analytics. Documents technical designs, governance, testing, and risk decisions while advising stakeholders and promoting responsible AI practices.

The Manager, Data Engineering - Finance leads a team to build and optimize finance data solutions and ensure data governance and architecture standards in a lakehouse environment.

Leads end-to-end design and delivery of Snowflake-centered data platforms, pipelines, analytics, reporting, APIs, and full-stack applications for clients. Responsibilities include solution architecture, technical implementation, data modeling, governance, performance and cost optimization, client advisory, discovery workshops, sales support, proof-of-concept development, project and team leadership, mentoring, and stakeholder communication. The role balances hands-on development with delivery leadership and business transformation across enterprise data and analytics initiatives.

Administers sensitive HRIS data, including employee records, new hires, job changes, acquisitions, audits, approvals, and onboarding. Provides managers with HR system guidance, fulfills ad-hoc reporting requests, supports compliance follow-ups, and assists with orientation, training, and process improvement. The role requires strong organization, attention to detail, communication, customer service, and the ability to manage multiple tasks independently in a fast-paced environment.

Provide analytics support to business lines and finance through data analysis, automated workflows, BI reporting, advanced analytics and generative AI. Engage stakeholders, present findings to senior leaders, improve processes, and implement cloud-based data solutions.

Lead design and delivery of ML and AI solutions by mining large datasets, building predictive models, and collaborating with product and analytics teams to drive data-driven business decisions and improve customer experience. Provide technical consulting, scale analytics solutions, and apply risk-aware decision making.

Leads Claims Data & Analytics strategy, identifying trends, interpreting claims results, and developing tools and models to improve operational efficiency and indemnity control. Advises senior executives through quantitative analysis and recommendations, manages resources and strategic initiatives, and oversees approximately 15 analysts through three direct reports. The role also drives talent development, stakeholder engagement, organizational capability building, and collaboration across business units and countries.

Deliver accurate, timely workforce reporting and insights for HR and business partners. Maintain data quality, enforce reporting governance, automate recurring reports using Excel/Power Query, support HR systems (Dayforce/Workday/etc.), validate data, and partner with stakeholders to improve reporting processes and data literacy.

Build and optimize enterprise ETL/ELT pipelines, data platforms, warehouses, models, and integration processes. Ensure data quality, governance, lineage, security, and performance across systems. Develop AWS-based data solutions and support automation, CI/CD, infrastructure as code, and monitoring. Partner with business and technical stakeholders to translate requirements into scalable solutions and deliver reliable analytics and reporting. Financial services knowledge and occasional travel may be required.

Lead retail and loyalty analytics by designing measurement frameworks, causal inference and experimental designs, building statistical, ML, and deep learning models, developing agentic AI and LLM solutions, and orchestrating scalable end-to-end analytical pipelines to improve customer engagement and marketing performance.

Lead retail and loyalty analytics by designing measurement frameworks, causal inference and experimental designs, building statistical, ML and deep learning models, and developing agentic AI solutions. Drive optimization and orchestration of end-to-end analytical pipelines, produce actionable insights for marketing and customer engagement, and present findings to business stakeholders.

Develop and scale customer-facing analytics, dashboards, reports, and data products for a healthcare AI platform. Partner with Product, Engineering, Sales, BizOps, customers, and other stakeholders to translate needs into data requirements and actionable insights. Lead reporting initiatives, resolve customer reporting issues, support high-stakes customer interactions, improve internal workflows, and help productize operational and clinical data into differentiated customer value.

Design and optimize data pipelines, cleanse and transform datasets, and build reliable reporting and analytics solutions using PostgreSQL, Metabase, and related tools. Collaborate with stakeholders to define KPIs, develop dashboards, troubleshoot data processes, and deliver actionable insights that support data-driven decisions.

The Database Research Expert will lead the architecture design for a data analytics platform, optimize database performance, and guide research on innovative database technologies.

The role involves analyzing software requirements, contributing to software development, mentoring developers, and leading design/code activities in a big data context.

Research emerging trends in AI-powered big data systems, develop proofs-of-concept, optimize performance, and collaborate on innovative solutions.

The Technical VP for AI & Data Storage will define technology roadmaps, lead research advancements, and mentor engineers in evolving storage systems integrated with AI.