Top Data Jobs in Toronto

Leads enterprise data governance execution across Global Technology & Solutions. Responsibilities include managing data quality, metadata, lineage, ownership, retention, disposition, risks, controls, issues, audit readiness, and regulatory requirements. The role translates enterprise policies into practical governance processes, coordinates stakeholders, monitors control effectiveness, resolves barriers, prepares executive reporting, and drives remediation without direct authority.

Develop and support finance data solutions across Azure, Databricks, Python, Spark, Azure Data Factory, and Microsoft Fabric. Lead technical design, data pipeline development, testing, integration, CI/CD deployment, and application implementation. Own product roadmaps and serve as a subject matter expert while coordinating with finance stakeholders, vendors, technical teams, and regulators. Build metadata-driven pipelines and Bronze/Silver/Gold data lake architectures for budgeting, forecasting, reporting, and analytics.

Leads customer-facing architecture engagements to modernize enterprise data platforms and build secure, governed, AI-ready solutions on Azure. Designs roadmaps across analytics, databases, data engineering, governance, and AI; guides proof-of-concept through production deployment; and supports migration, resiliency, performance, security, cost optimization, and scaled adoption. Partners with account, engineering, delivery, and customer success teams to remove technical blockers and develop reusable solution patterns.

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.

Design and implement an end-to-end lakehouse and data warehouse platform integrating Genesys Snowflake and Verint data. Build Databricks and Delta Lake pipelines, Kimball dimensional models, ingestion strategies, quality controls, orchestration, monitoring, and recovery processes. Lead migration from legacy CCARS pipelines through parallel runs and decommissioning while ensuring scalability, performance, auditability, and data integrity. Collaborate with engineering, reporting, QA, and business stakeholders.

Design and deploy AI-powered applications, agentic workflows, and enterprise automation solutions. Build data pipelines, APIs, microservices, and AI components using Python, SQL, and related technologies. Integrate LLMs, machine learning models, RAG, embeddings, vector databases, and agent frameworks in a regulated financial-services environment. Collaborate with architects, product owners, data scientists, and engineers while documenting solutions and researching emerging AI capabilities.

Design, build, and operationalize enterprise data solutions across AWS and Snowflake. Develop Python and DBT ETL/ELT pipelines, integrate diverse data sources, and manage Apache Airflow workflows. Contribute to data warehouses, marts, governance, catalogs, and quality initiatives while applying modern and legacy data platform expertise in an agile environment.

Maintains project and amendment setup in the accounting system, processes change orders and fee increases, supports contract administration and audits, prepares project reports, identifies discrepancies and risks, and collaborates with project managers and Finance and Accounting teams.

Coordinates product data for furniture manufacturing by translating engineering drawings into bills of materials within Jeeves ERP. Maintains item master records, BOMs, work processes, order-processing parameters, and product data changes. Supports engineering special approvals, identifies data issues, applies quality standards, and recommends process improvements. Collaborates with engineering, production, scheduling, inventory, and operations teams while developing proficiency in manufacturing processes and ERP-based product data management.

Lead the design, development, testing, implementation, and support of cross-functional applications and scalable Azure data platforms. Build and govern solutions using Databricks, Synapse, Delta Lake, PySpark, Spark SQL, Azure Data Factory, and Python. Translate business requirements into technical designs, conduct code reviews, ensure security and observability, and coach developers through indirect leadership while partnering with business and technology stakeholders.

Leads credit risk strategy initiatives through complex data analysis, risk monitoring, reporting, dashboards, audits, and testing support. Uses SQL and Python to generate risk insights and recommendations, evaluates processes and controls, and helps implement data, analytics, AI, and emerging technology solutions. Independently leads cross-functional projects, communicates findings to senior stakeholders, and supports practical improvements to credit risk decision-making and governance.

Lead the design, development, optimization, and maintenance of scalable Azure Databricks data infrastructure and pipelines. Build reliable pipelines across policy, billing, claims, and finance data; implement quality, security, testing, observability, and disaster recovery practices; optimize platform performance; and collaborate with data scientists and business stakeholders. Provide technical leadership, mentor engineers, and apply modern data engineering and AI-assisted development practices.

Designs, builds, and maintains scalable batch and streaming data pipelines across AWS and GCP. Develops ETL/ELT workflows using Python, PySpark, and advanced SQL; optimizes performance, storage, and query execution; implements data quality and governance controls; and supports data lakes, warehouses, and ML workflows. Collaborates with data scientists and ML engineers on MLOps automation and production deployments while maintaining CI/CD, Docker, infrastructure standards, and technical documentation.

Lead end-to-end development and deployment of fraud detection and risk analytics using machine learning and Generative AI. Design probabilistic, anomaly detection, ensemble models; build and monitor RAG/LLM systems; fine-tune and deploy models on Azure; collaborate with engineers and business partners to operationalize solutions and review peers' work.

Leads data engineering and ML Ops strategy, infrastructure, pipelines, model deployment, monitoring, and optimization. Oversees data engineers, establishes data-centric practices, ensures scalable and secure systems, and partners with data scientists and cross-functional teams. Responsibilities include roadmap planning, team development, compliance, resource allocation, continuous improvement, and adoption of modern cloud, data engineering, and machine learning technologies.

Own the analytical layer connecting raw data to business decisions across 13 retail brands. Build SQL and Python data models, semantic layers, KPIs, and reconciled metrics across ERP, web analytics, and operational systems. Validate data quality, scope requests, partner with engineering and stakeholders, and use AI tools to accelerate analytics workflows while reviewing outputs critically.

Staff Data Analyst responsible for raising analytical standards across the team, building user segments, improving dashboards and automated monitoring, and connecting behavioral insights to revenue, retention, cost, and growth goals. The role partners on experimentation, standardizes analytical methods, reviews complex work, mentors analysts, and communicates decision-ready narratives to senior stakeholders. Advanced SQL, Python, experimentation expertise, and strong user analytics skills are required.

Leads pension member and contribution data reconciliation, reporting, quality controls, governance, and root-cause remediation. The role analyzes large datasets, develops dashboards and data quality indicators, supports statutory reporting and pension valuations, and serves as a subject matter expert for internal teams, employers, and stakeholders. It also mentors junior staff, documents data standards and controls, coordinates issue resolution, and translates complex pension data issues into business impacts and recommended actions.

Leads pension data governance and operations, establishing data standards, quality metrics, controls, reconciliations, issue remediation, and executive reporting. Partners with pension operations, technology, finance, actuarial, legal, privacy, risk, employers, and members to improve data integrity, compliance, service delivery, and operational outcomes. The role also supports implementation of a new pension administration solution, develops training and service practices, and leads teams responsible for member transactions, pension payroll, and reporting and analytics.

Leads UPP’s enterprise data strategy and capabilities, including governance, quality, engineering, integration, operations, metadata, analytics enablement, and cloud data platforms. Builds and manages a high-performing data team, establishes standards and roadmaps, improves data reliability and discoverability, and partners with business, technology, cybersecurity, architecture, privacy, and AI teams. The role supports trusted reporting, automation, and responsible AI while enabling business users to work with data safely and effectively.

Manages pension data quality operations, controls, validation, issue resolution, governance, reporting, and continuous improvement. Oversees data quality metrics, root-cause analysis, remediation, audit documentation, and risk-based prioritization across employer and member data. Partners with Pension Services, Technology, Data, and employers to improve source data and system processes. Leads and develops a team responsible for monitoring, testing, defect management, and data quality controls while ensuring pension administration accuracy, compliance, and service-level performance.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Develop and maintain ETL/ELT data pipelines within a cloud-based data lakehouse architecture. Responsibilities include data transformation, software lifecycle participation, testing, documentation, third-level support, proof-of-concept development, operational reporting, and semantic-layer support. The role requires advanced SQL and Python/PySpark, experience with Spark, Delta Lake, Parquet, multiple databases, privacy-regulated data, and multi-cloud environments.

Oversee the accuracy, maintenance, integration, and optimization of hotel supplier data across product platforms. Serve as the senior subject-matter expert and final quality-control point, troubleshoot discrepancies, coordinate resolutions, validate content with suppliers, support testing of new products and integrations, and report on data quality and processing performance.

Leads enterprise stress-testing aggregation, regulatory reporting, data governance, reconciliations, controls, and analytics modernization. Translates regulatory and business requirements into mappings, data specifications, test cases, controls, and reporting solutions. Uses Python, SQL, Databricks, and Power BI to automate analysis, validate transformations, build governed datasets, and support parallel testing. Coordinates cross-functional delivery, investigates issues, maintains audit-ready evidence, and communicates risks and decisions to senior stakeholders.