Top Data Jobs in Toronto

Build and maintain integrations and data pipelines connecting client databases, APIs, and SaaS tools. Extract, clean, transform, and move messy real-world data to feed retrieval and agentic AI systems, apply security and privacy best practices, and document reproducible integrations.

Lead and grow a Data Engineering team to modernize enterprise data platforms (Snowflake/Databricks), deliver secure scalable batch and real-time pipelines, enable DataOps/MLOps, establish CI/CD and IaC practices, monitor platform health, and drive strategic platform migrations and performance optimizations with cross-functional stakeholders.

Lead the development and delivery of AI-driven product capabilities, transforming data into actionable insights while mentoring a high-performing team of data scientists and engineers.

As a Data Engineer, you will design and implement scalable data pipelines and solutions, collaborate with teams, and apply new technologies in an Agile environment.

The Data Engineer will design scalable data pipelines, manage data solutions, collaborate with teams, and ensure project delivery on time while applying new technologies.

Design, develop, and optimize big data solutions using Databricks, Apache Spark, and cloud platforms. Collaborate with teams to ensure high-performance data architectures.

The VP, Head of Policy & Data Operations manages data governance and operations for proxy voting policies, ensuring data integrity and operational excellence while collaborating across teams to optimize processes and manage risks.

The Data Engineer will design and maintain data pipelines on cloud data warehouses, manage integrations with advertising platforms, ensure data quality, and collaborate with cross-functional teams to meet reporting needs.

Design, build, and optimize lakehouse data pipelines and curated data products using Python and SQL. Improve performance, observability, governance, and cost-efficiency. Own data modeling, orchestration, data quality, and secure access patterns. Partner with product, engineering, and analytics teams, troubleshoot production issues, and mentor junior engineers to advance the organization’s data platform and AI readiness.

Serve as a senior IT business analyst for the Data Lab: elicit and document business needs, define IT/data solutions, estimate impacts/effort, support development, QA and acceptance testing, maintain user stories and product backlog, support change management, and produce clear functional documentation for stakeholders.

Build and maintain Power BI semantic models and Microsoft Fabric data pipelines from Salesforce and GTM systems. Deliver analyses that inform GTM decisions, reconcile data across systems, own metric definitions, and document lineage and runbooks. Provide ad hoc investigations and present findings to leaders.

Design, build, and maintain scalable cloud data pipelines and architectures. Develop and optimize ETL/ELT workflows, manage data warehouses (primarily BigQuery), implement governance and security, collaborate with engineers and analytics teams, enable ML/AI use cases, and maintain documentation, lineage, and high-availability pipelines.

Develop strategic recommendations and go-to-market plans for Autodesk's AEC data capabilities by analyzing customer, market, and competitive insights. Partner with Product, Platform, Sales, Marketing, and Customer Success to inform roadmaps, support adoption, enable partnerships, and validate opportunities with industry stakeholders. Lead cross-functional initiatives and stay current on emerging technologies, standards, and AI-driven trends shaping AECO data management.

Early-career business analyst on a multi-year investment platform transformation. Support requirements gathering, hands-on data analysis, testing, and documentation across conversion, data integration, data platform (Snowflake) and custom solutions. Engage with stakeholders, run test plans, investigate data discrepancies, and contribute to AI-driven process improvements and change management.

Lead business and data analysis for a multi-year investment platform transformation into Aladdin and a Snowflake data platform. Elicit and document requirements, perform hands-on data profiling and reconciliation, design extensible capabilities, own delivery and operability, define and execute test plans, and support current-state systems to ensure correct migration and ongoing operation. Drive stakeholder workshops, change management, and documentation to ensure solutions are production-ready and maintainable.

Build and operate backend-heavy full-stack applications, APIs, dashboards, and data pipelines. Integrate ML/AI models into production workflows, design data models, containerize and deploy services with Docker/Kubernetes, improve reliability and observability, and collaborate with research, data science, product, and design teams.

Manage end-to-end product data lifecycle in the ERP, maintain product master data and pricing, support product launches and cross-functional validation, steward digital assets and compliance documentation, monitor data quality, and drive process improvements to ensure accurate, timely product information for merchandising, marketing, procurement, QA, and sales.

Design, build, and maintain production data pipelines using Airflow/Cloud Composer and BigQuery; define data models and optimize queries; deliver custom solutions from POC to production; collaborate with Data, Product, and Customer Success; deploy dashboards/data apps; continuously improve data infrastructure and leverage AI tools to accelerate development.

Build and maintain core financial backend systems (trading, ledger, billing), transaction processing, reconciliation, and portfolio state management. Design and scale real-time and batch data pipelines, architect APIs and backend services, ensure reliability and observability, and contribute to distributed system design (event-driven, microservices, streaming). Partner with ML teams as needed.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Design, build, and operate cloud-native, serverless ClickHouse database infrastructure. Develop and maintain Kubernetes operators, improve metrics pipelines, support data ingestion/transformation (Spark/Kafka), collaborate with core DB and data plane teams, and ensure scalable, highly available distributed systems with production on-call responsibilities.

Design, build, and deploy scalable, secure data solutions on-premise and in Azure. Collaborate with engineering, security, and ops to implement modern data practices (Databricks, Snowflake, Python, SQL), produce solution designs, support project delivery, and promote DevSecOps and cloud best practices.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Evaluate and label model-generated and human-written software engineering outputs for coding, debugging, and codebase-related tasks. Assess agent trajectories, prompt models for complex coding tasks, proofread and improve responses, and report quality and performance trends. Part-time contractor role contributing to model development.

The Data Product Manager will lead the development of enterprise data products by translating business needs into scalable data solutions, managing analytics projects, and coordinating with global teams to ensure efficient delivery and optimization of data initiatives.