Top Data Jobs in Toronto

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

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.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

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.

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.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Build and own marketing measurement, attribution, experimentation, and analytics for performance, influencer, and lifecycle marketing. Responsibilities include creating dashboards, debugging attribution across advertising platforms and CRM systems, designing incrementality and A/B tests, analyzing CAC, ROAS, payback, cohorts, LTV, and channel efficiency, developing attribution taxonomies and instrumentation, and translating findings into recommendations for marketing, product, finance, and executive stakeholders.

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

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.

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.

Analyze product usage, feature adoption, telemetry, and financial data to generate actionable insights. Own data modeling, dashboards, KPI development, and end-to-end analytics delivery while partnering with stakeholders on product initiatives. Mentor associate-level analysts and maintain strong execution metrics for analytics work. The role requires advanced SQL and Python, Databricks and PySpark expertise, visualization experience, and SaaS product analytics experience.

Build highly available, scalable distributed systems and real-time services supporting Internet-wide scanning, data ingestion, APIs, security research, and machine learning initiatives. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, and cloud or on-premises services; productionizing prototypes; collaborating with engineering, product, machine learning, and security teams; and maintaining reliable, observable systems.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

Serve as TMX Datalinx's technical Salesforce SME: administer and optimize Salesforce, manage configurations, integrations, data quality, reporting, and user enablement; lead enhancements, governance, and cross-functional projects while mentoring junior admins.

Lead technical strategy and hands-on development of large-scale optimization and ML systems (routing, scheduling, resource allocation). Design MILP/VRP/constraint solutions, integrate forecasting with optimization, deploy on cloud/lakehouse, mentor a data science team, and partner with stakeholders to deliver measurable operational improvements.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Lead end-to-end data and ML pipelines for onboarding and fraud decisions: ingest streaming and batch sources, build feature platform and entity resolution, productionize ML models, enforce PII/data governance, and set technical direction while mentoring engineers and ensuring low-latency, reliable serving.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.