Top Data Jobs in Toronto

Manage end-to-end data migrations for new client implementations: extract, cleanse, map, validate, and import data. Work with implementation teams and clients to resolve data quality issues, document processes, support testing and go-live, and continuously improve migration practices.

Lead implementations of Proofpoint Information Protection to meet information protection and compliance objectives. Engage enterprise customers, configure Email/Cloud/Endpoint DLP, conduct DLP and data exfiltration investigations, build playbooks, mentor team members, and document best practices while coordinating cross-functional teams to ensure successful project outcomes.

Lead and grow a data engineering team to design, build, and operate scalable data platforms (ETL/ELT, warehouses, lakes) powering Customer 360, personalization, analytics, and reporting. Partner with business and product teams, deliver enterprise data products, present to executives, and drive large-scale change while ensuring governance, privacy, and regulatory compliance.

Owner of end-to-end data analysis and delivery for product and client solutions. Design queries, datasets, reports and dashboards, protect recurring data deliverables, partner with Product/Sales/Technology, mentor junior analysts, and apply automation/AI to improve workflows and inform product strategy.

Design and build an AWS-based lakehouse and scalable enterprise data platform. Create governed data products and automated Python/SQL pipelines. Establish standards for data quality, governance, security, observability, and CI/CD. Partner with engineering, product, finance, and business teams to enable analytics and AI and define the platform's long-term vision.

The Data Analyst will analyze large datasets, collaborate with stakeholders, develop data processes, and support data-driven decision-making to improve business efficiency.

Lead and grow a team of DBAs and data engineers to ensure reliable, scalable, and secure data infrastructure. Oversee heterogeneous databases, define cloud and containerization strategies, drive IaC and automation, support data pipelines and ETL/ELT, and implement high availability and disaster recovery practices.

The Senior Data Engineer will scale the data ecosystem, construct data models, build systems for KPI tracking, and ensure data safety.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Contract HR Data Administrator to digitize and migrate employee files into Workday. Duties include reviewing, scanning, organizing, uploading records, verifying accuracy, tracking migration progress, flagging missing documentation, and maintaining confidentiality. Provide progress updates and follow established naming conventions and data structures.

Lead a hybrid engineering and data platform team to build scalable software and data infrastructure. Oversee Power Platform deployments, data architecture, governance and compliance (GDPR/HIPAA), BI/analytics, DevOps automation, and integration of AI/LLM and agentic solutions to drive intelligent automation and platform scalability.

Use legal training to create and evaluate AI training data: perform legal research and analysis, provide annotation and feedback, support senior team members, communicate insights, participate in stakeholder presentations, and advocate for Responsible AI.

Manage call forecasting, scheduling strategy, and intraday processes for a multi-skill contact center. Lead an intraday team, optimize workforce capacity models using analytics, implement Google CES, leverage NICE and Clearview, produce regular and ad-hoc performance reports, and lead small projects to improve service levels, productivity, and automation outcomes.

Lead end-to-end improvements to pricing ML systems: analyze market and vehicle data, design features, build/validate/deploy production ML models, run experiments, and partner cross-functionally to improve margin and conversion. Expand work into lending, fraud, logistics, search, and recommendations as needed.

Design, build, and optimize Databricks-based data and ML platforms. Own ETL pipelines, Delta Lake lakehouse, MLflow model lifecycle, MLOps, cluster optimization, RAG/LLM solutions, BI integration, and data governance with Unity Catalog.

Design, prototype, and productionize machine learning models for real-world problems using structured and unstructured data. Implement scalable ML systems, apply statistical methods, rapidly prototype research papers, and collaborate with engineering teams to deliver data-driven solutions.

Lead data science engagements: partner with client executives, identify high-impact projects, design end-to-end data pipelines and models, build KPIs and production dashboards, mentor teams, and deliver scalable data products driving business impact.

Perform data labeling and validation, maintain databases/spreadsheets, research and compile information, communicate findings, provide administrative support, and collaborate with data science and engineering teams.

Design, build, and optimize ETL pipelines, data schemas, and data tools to support analytics and data science. Tune SQL and databases for performance and collaborate with teams to scale data infrastructure using Snowflake, Python, and related tools.

Design, prototype, and productionize machine learning models from structured or unstructured data, build data products for business insights, and apply statistics and ML theory to solve client problems.

Design, build, and maintain production data infrastructure and pipelines; automate workflows; enable cross-team data access; perform large-scale data wrangling, statistical analysis, and visualization to deliver business insights.

Lead and manage a Data Engineering team: hire, mentor, and evaluate associates; oversee vendors; define and implement data warehouse architecture, ETL/data pipelines, and database systems; use Microsoft SQL Server and GCP services (BigQuery, Dataproc, Cloud Data Fusion); collaborate with cross-functional stakeholders and report area progress to executive leadership.

Senior Data Engineer responsible for designing and validating data solutions, consolidating disparate data sources, documenting functional specifications, reviewing data models, supporting production deployments, investigating data quality issues, and collaborating with stakeholders and technology leads to ensure data completeness and lineage.

Design, develop, and maintain data engineering solutions across the SDLC using Python (primary) and Scala (secondary). Build distributed data pipelines on Hadoop/Spark ecosystems, extend data processing platforms (Spark SQL, Hive, Starburst), manage CI/CD, and deploy on Kubernetes/OpenShift. Collaborate with stakeholders in Agile teams and ensure maintainable, reusable code.

Own the data platform product portfolio, prioritize datasets and capabilities, define schemas and governance, partner with engineering and analytics, enable trusted consumable data, run discovery with domain experts, track data health and adoption, and drive cross-team alignment to support downstream products and analytics.