Top Data Jobs in Toronto

Own the end-to-end data architecture for contact center telephony and workforce-management data. Design Bronze/Silver/Gold lakehouse flows, Kimball dimensional models, ingestion and watermarking strategies, reconciliation, integrity controls, orchestration, and failure recovery. Build and optimize Databricks and Delta Lake pipelines integrating Snowflake, APIs, SFTP, and vendor files. Support parallel-run migration, legacy decommissioning, and communicate architecture decisions to engineering and business stakeholders.

Designs, builds, automates, and optimizes enterprise cloud data pipelines and platforms. Responsibilities include ETL/ELT, DataOps, CI/CD, data migration, data modeling, data warehousing, lakehouse architecture, database administration, performance tuning, and production support. The role uses Databricks, Azure services, PySpark, SQL, and multiple cloud platforms to deliver scalable structured and unstructured data solutions.

Provide L3 production support for Salesforce Data Cloud and Loyalty Management platforms. Troubleshoot incidents, APIs, integrations, SQL queries, and upstream or downstream application issues; perform root cause analysis and resolve problems within SLAs. Manage critical incidents, coordinate stakeholders, and support ITIL-based incident and change processes. The role requires hands-on Data Cloud development, Apex, basic development skills, and rotational on-call support.

Leads capital and credit risk data analytics, risk-system governance, production support, data reconciliation, reporting, and control processes. Uses Python, SQL, and Power BI to analyze data, develop reporting solutions, identify process improvements, and provide actionable risk insights. Coordinates system changes, testing, documentation, regulatory audit support, stakeholder alignment, and root-cause investigations across risk technology environments.

Architects, configures, customizes, and supports scalable Salesforce Data Cloud solutions. Defines data models, schemas, governance, quality, security, and integration strategies; develops custom features and third-party integrations; troubleshoots deployments and optimizes performance. Provides technical leadership to Salesforce developers and administrators while aligning solutions with client and business objectives.

Leads Commercial Banking’s data foundations organization, including data strategy, architecture, modeling, ETL pipelines, data quality, governance, operations, modernization, and business-managed applications. Builds trusted, scalable data assets supporting reporting, analytics, AI, and decision-making. Partners with business, technology, governance, and analytics teams while managing service levels, risk controls, platform improvements, and a high-performing data team.

Supports Treasury data integrity through reconciliation, reporting, data quality monitoring, issue remediation, manual adjustments, controls, and process improvements. Analyzes variances, assists with regulatory reporting, documents activities, addresses stakeholder inquiries, and supports Treasury data management projects. The role also maintains internal controls, assists auditors, and develops technical or business solutions under SOX guidelines.

Designs, builds, and maintains scalable data platforms, architectures, and pipelines using Microsoft Fabric, Databricks, and Snowflake. Responsibilities include data modeling, ETL/ELT, workflow optimization, security and compliance, data quality improvements, and collaboration with analysts, scientists, engineers, and business stakeholders.

Leads end-to-end analytics initiatives, including stakeholder discovery, data validation, dashboard and reporting design, deep-dive analysis, KPI development, experimentation, and executive presentations. Collaborates with data engineers on modeling and reporting quality, establishes analytics standards and governance, mentors junior analysts, and translates complex business questions into actionable recommendations.

Supports client analytics engagements by analyzing data, building dashboards and recurring reports, tracking KPIs, validating data quality, documenting metrics, and communicating insights. Collaborates with engineers, analysts, product managers, designers, and stakeholders to translate business questions into analytical requirements and improve reporting workflows. The role also provides exposure to consulting, digital product development, AI, UX, and data governance.

Supports enterprise data platforms, ETL processes, reporting environments, and data integration initiatives. Responsibilities include monitoring data loads, troubleshooting database and reporting issues, developing SQL queries and procedures, validating data, creating Power BI dashboards, supporting Snowflake and Microsoft data platforms, and contributing to data governance, migration, automation, and quality improvements.

Supports reporting production, data analysis, and analytics operations across the COR.IQ product portfolio. Develops and maintains SQL and Qlik queries, data extracts, scripts, and reporting enhancements; investigates data quality issues and operational anomalies; supports product onboarding and client requests; and provides backup coverage for the Data Analyst 3. The role also develops expertise in fraud analytics, reporting systems, product operations, and continuous improvement.

Analyze full-funnel B2B marketing performance using dashboards, SQL, attribution modeling, and CRM data. Build Tableau or Looker Studio reporting, query Redshift data, and apply Python, AI APIs, prompt engineering, and automation to streamline data processing and performance summaries. Partner with campaign, web, and sales operations teams to improve tagging, KPI consistency, lead velocity, and conversion insights.

Performs data engineering across modeling, profiling, quality, ingestion, ETL, metadata, lineage, and governance. Develops complex data models, ETL frameworks, and enterprise data solutions; supports testing, deployment, release management, architecture reviews, and issue resolution. Partners with technical and business stakeholders, ensures compliance with data and security standards, leads engineering initiatives, mentors team members, and communicates complex technical details to nontechnical audiences.

Lead design, implementation, and operation of a Databricks-based enterprise technology reference data platform. Oversee data ingestion, integration, transformation, pipelines, security, observability, and platform reliability. Drive standards, enable data products and reporting, partner with stakeholders, and build a high-performing data engineering and DataOps team to support analytics and AI use cases.

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.

Develop predictive weather, spatio-temporal, and time-series models using meteorological and client sales data. Analyze complex datasets, optimize machine learning performance with distributed frameworks, and manage models through deployment and monitoring. Collaborate with sales, marketing, product, engineering, and revenue teams to deliver scalable B2B data products, APIs, and actionable commercial insights. Document model architectures, data lineage, and decision logic.

Leads enterprise data risk oversight by designing frameworks, standards, methodologies, metrics, and reporting tools. Provides independent challenge of data, AI, privacy, governance, quality, lineage, access, third-party, and ethical risks across the organization. Aggregates systemic exposures, oversees significant issues, supports governance committees and Board reporting, and ensures alignment with OSFI guidance and privacy laws. The role influences senior stakeholders, coordinates with AI and technology risk teams, and does not own first-line controls or remediation.

Develop machine learning and advanced analytics models for algorithmic trading across multiple asset classes. Build predictive models and production ML pipelines, analyze large datasets, research techniques such as generative AI and deep learning, and collaborate with trading, investment banking, and corporate banking stakeholders. Communicate insights to business leaders and visualize findings to support strategic decisions and improve trading performance.

Develop machine learning and advanced analytics models for algorithmic trading across multiple asset classes. Build predictive models and reproducible production pipelines, analyze large datasets, research techniques such as generative AI and deep learning, and communicate insights to trading, banking, and senior management stakeholders.

The Vice President, Data Scientist will develop and implement machine learning models to enhance algorithmic trading strategies and provide data-driven insights for Investment Banking and Corporate Banking.

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

Design, build, and scale IAM platform services and APIs for authentication, authorization, identity lifecycle, access governance, and compliance. Integrate identity providers and authorization systems, improve scalability and observability, design machine and service authentication, participate in architecture reviews, and mentor engineers.

Lead enterprise Data, AI, and Generative AI architecture from concept through production. Design scalable, secure cloud-native platforms, AI/ML deployment and governance capabilities, and reusable architecture patterns. Partner with engineering, data, security, product, and business teams to guide complex initiatives, assess technologies, manage risks, and establish standards. Mentor architects and technical leaders while influencing strategic technology decisions across teams.

Build, operate, and optimize AWS-based data ingestion pipelines from Aurora MySQL into Snowflake. Responsibilities include infrastructure engineering, reliability and observability, SLOs, incident response, infrastructure as code, CI/CD, performance and cost tuning, source onboarding, and security controls for payment-compliant environments. The role also involves applying AI tools to improve development, testing, runbooks, and documentation.