Top Data Jobs in Toronto

Lead a data science team to design and deliver merchant registry and profiling ML capabilities for onboarding and monitoring. Define strategy, guide model development and validation, promote reusable data assets, partner with Product and Engineering, drive Agile delivery, hire and mentor team members, and communicate impact to senior leadership to scale production ML systems on modern cloud/data platforms.

Partner with Marketing, Sales, Operations, Strategy, and other teams to define analytical needs and deliver insights. Analyze customer behavior, product usage, engagement, retention, marketing performance, and operational outcomes using SQL, Python, Snowflake, visualization tools, and AI. Build dashboards and reports, identify trends and opportunities, and communicate practical recommendations to technical and nontechnical stakeholders.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Partner with Finance, Engineering, Sales, Product, and Marketing to deliver reliable data for reporting and decision-making. Improve manual workflows, validate data accuracy, and design scalable solutions using modern data modeling practices. Support financial and revenue-related analytics, including ARR and revenue recognition, while balancing immediate fixes with long-term architecture.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Perform lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to validate data assets. Implement advanced SQL and PySpark pipelines, orchestration, data quality, observability, lineage, masking, and row- and column-level security. The role also supports vector search, embeddings, cloud data onboarding, and regulated-domain use cases.

Designs and leads scalable AWS and Databricks data pipelines, data models, quality frameworks, monitoring, testing, and production deployments. Investigates ambiguous data and business problems independently, translating requirements into secure, reliable technical solutions. Mentors engineers, establishes architecture and coding standards, and partners with architecture, analytics, product, and business teams. Supports AI readiness, data unification, metadata strategy, and enterprise integration initiatives.

Leads total fund data science, AI-enabled analytics, semantic modeling, governed data products, and investment reporting capabilities. Designs analytical models, ETL/ELT pipelines, semantic metric layers, data marts, and LLM/RAG interfaces across enterprise platforms. Provides technical oversight, governance, quality controls, stakeholder influence, and strategic delivery across Finance, Investments, Technology, and Data. Coaches colleagues and advances responsible AI adoption, reporting automation, investment insight generation, and operational efficiency.

Administer Microsoft Purview catalogs and glossaries, maintain metadata and lineage, execute SQL-based data profiling and quality monitoring, investigate issues, develop governance automations and API integrations, prepare reporting materials, and maintain audit-ready SOC1 control evidence, exception logs, configurations, and procedures.

Supports enterprise data governance and Data Security Posture Management initiatives by classifying sensitive data, maintaining metadata and catalogs, documenting lineage, monitoring data quality and risk, supporting retention policies, and governing structured and unstructured data. The role uses SQL and governance platforms to investigate findings, improve data protection, support audits, collaborate with stakeholders, and promote responsible data use and literacy.

Support Salesforce users, troubleshoot platform issues, manage support tickets, maintain user access, validate and cleanse data, prevent duplicate records, and help ensure data integrity and completeness. The role also assists with functional testing of Salesforce developments and requires strong communication, organization, customer service, analytical, and problem-solving skills. Employees work onsite four days per week.

Design, build, and scale real-time and batch data infrastructure and pipelines (Kafka, Databricks, Snowflake) to power analytics, reporting, and AI-powered data experiences. Drive data modeling, governance, observability, and best practices while partnering with cross-functional teams and mentoring engineers.

Leads Versapay’s enterprise data strategy, architecture, governance, quality, accessibility, and AI readiness. Builds unified data infrastructure, semantic models, catalogs, asset registries, and governance standards while enabling secure internal and commercial data products. Partners with Product, Engineering, Commercial, Finance, and Legal, and leads the Data Platform Team. Owns data maturity roadmaps, warehouse cost management, AI infrastructure readiness, and executive-level data strategy.

Develop and deploy algorithmic and machine learning solutions for Lyft’s mapping products, improving route recommendations, traffic prediction, and travel-time estimates. Responsibilities include data exploration, feature engineering, experimentation, production ML systems, real-time inference, batch pipelines, monitoring, optimization, and metric development. The role partners with Engineering, Product, Operations, and Science teams to translate complex mobility and geospatial challenges into scalable solutions.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Labels, categorizes, validates, and quality-checks structured and unstructured data used to train AI and machine learning models. Maintains dataset accuracy, consistency, documentation, and version control while following detailed annotation guidelines. Collaborates with AI engineers, data scientists, and quality teams, identifies errors, provides process feedback, and supports continuous improvement of annotation workflows.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

Lead design and delivery of enterprise-scale AI/ML solutions for Ethoca, building and governing models (NLP, generative, multi-modal) to reduce chargebacks, improve dispute resolution, and scale production systems across cloud environments.

Lead development, deployment, and monitoring of end-to-end ML/DL/AI solutions. Responsibilities include data extraction, cleaning, modeling, evaluation, production deployment, documentation, and presenting results. Collaborate across teams, build prototypes and POCs, protect IP, and disseminate knowledge externally.

Own the technical sales motion for NVIDIA-focused enterprise AI infrastructure deals. Lead discovery, architecture, proof-of-concept design and execution, technical closes, partner and field SE enablement, troubleshooting, escalation, customer workshops, and product feedback. The role requires expertise in NVIDIA systems, storage, data platforms, Linux, parallel file environments, GPU clusters, and AI pipelines, with Bay Area availability for on-site partner or customer sessions.

Use data science, experimentation, forecasting, and analytical frameworks to improve Lyft’s airport marketplace. Analyze large datasets, define product health metrics, identify growth opportunities, recommend technical and product solutions, and measure feature impact. Collaborate with engineering, product, design, and other scientists while presenting actionable findings to senior leadership and cross-functional stakeholders.

Own ETF data quality and accuracy across VettaFi’s consumer platforms. Audit and improve LLM-generated classifications, descriptions, and data extractions; build and maintain a unified ETF taxonomy and ontology; manage market data vendors; collaborate with Product, Engineering, and Marketing; and establish KPIs for data quality, completeness, timeliness, and platform performance.