Top Data Jobs in Toronto

Build and maintain scalable data pipeline architecture, process complex datasets, improve data delivery and infrastructure, and support data initiatives across engineering, product, business intelligence, and data science teams. Develop extraction, transformation, and loading infrastructure using Spark, EMR, Kafka, and other big data technologies while optimizing production-scale data and machine learning pipelines.

Designs end-to-end data solutions spanning ingestion, transformation, storage, modeling, and delivery for BI, analytics, AI/ML, and external consumers. Creates data flow diagrams and models, guides architectural and technology decisions, and collaborates with engineering, analytics, AI, governance, and business teams. Ensures solutions are scalable, secure, well-documented, performant, and compliant with financial-services governance and regulatory requirements using AWS, Snowflake, dbt, Alation, and Tableau Data Catalogs.

Leads integration of two major financial data product launches into the AlphaSense platform. Defines zero-to-one product lifecycles, user stories, milestones, roadmaps, and PRDs; translates investment research workflows into data architectures; manages resources and delivery across teams; mentors product managers; validates solutions with customers and engineering; and drives competitive intelligence, prioritization, and cross-functional alignment.

Design, build, and support production data feeds and integration pipelines using Python, SQL Server, Snowflake, REST APIs, SFTP, Docker, and AWS ECS. Develop incremental-loading workflows, apply Data Vault modeling, troubleshoot end-to-end data pipelines, and implement validation, monitoring, logging, error handling, and retries. Collaborate with technical and business stakeholders while maintaining pipeline and operational documentation.

Lead data conversion activities for global ERP deployment projects, including data cleansing, normalization, mapping, validation, duplication checks, extraction, transformation, and loading. Establish conversion standards and processes, prepare data load files, monitor data quality, report results, and identify process efficiencies. Collaborate with ERP leadership, business stakeholders, master data, engineering, integration, and IT teams. The hybrid role requires three days onsite in Mississauga and approximately 10% travel.

Lead the architecture and implementation of a Microsoft Fabric data platform, including migration from Azure Synapse, secure networking, near-real-time ingestion, data protection, and Medallion architecture. Design ingestion from Oracle and SQL Server systems, guide capacity and cost strategy, validate GCC High capabilities, mentor data engineers, review technical work, resolve complex challenges, and communicate architecture decisions to client stakeholders.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Sell Salesforce Data solutions to Canadian federal government agencies in Ottawa. Responsibilities include developing territory plans, building pipeline, managing public-sector accounts, identifying expansion opportunities, explaining data solutions to business and technical stakeholders, collaborating with partners and internal teams, addressing client issues, and reporting business progress. The role requires extensive enterprise technology sales experience, preferably with data platforms and Canadian public-sector clients.

Design and maintain scalable backend, full-stack, and AWS data infrastructure supporting millions of daily events and hundreds of integrations. Set technical direction for data pipelines, lead architecture and planning, troubleshoot production issues, collaborate across teams, manage work in Jira, research improvements, and mentor engineers. The role owns initiatives end to end while developing reliable, cost-efficient serverless systems using Node.js, TypeScript, React, and AWS services.

Define the architecture and technical direction of Coalition’s enterprise data platform. Build scalable Snowflake and dbt infrastructure, governed data models, batch and streaming pipelines, reusable data products, feature stores, semantic models, and AI data foundations. Establish standards for data quality, security, lineage, observability, and regulated workflows. Partner across engineering, product, underwriting, actuarial, security, and business teams while mentoring engineers and guiding cross-team architecture.

Owns Simbe’s product data management and Arena PLM administration for robotic hardware programs. Responsibilities include authoring and driving ECOs, MCOs, and deviations through approval; maintaining item masters, BOMs, approved vendor lists, part lifecycles, and documentation integrity; enforcing revision, numbering, and change-control standards; and coordinating implementation with Engineering, Operations, Supply Chain, suppliers, and contract manufacturers.

Build and scale real-time and batch data pipelines, data platforms, and analytics-ready datasets using Kafka, Databricks, Snowflake, Python, and SQL. Design modern data models, ensure data quality and observability, develop AI-powered data experiences, and support business-critical reporting and analytics. Collaborate cross-functionally, improve platform performance, champion engineering best practices, and mentor teammates.

Manage CRM customer data migrations from onboarding through completion. Responsibilities include analyzing source Excel data, creating import rules, validating and reconciling migrated data, documenting mappings and transformation logic, resolving discrepancies, and coordinating with customers, engineers, sales, onboarding, and support teams. The role requires strong Excel and Google Sheets skills, analytical problem-solving, attention to detail, customer service, and effective communication.

Designs and manages scalable data integration pipelines, governance controls, semantic enrichment, and orchestration systems for enterprise AI and analytics. Builds real-time data flows, APIs, connectors, RAG and embedding pipelines, vector database integrations, monitoring, audit trails, and AI guardrails. Partners with engineers, governance teams, and business stakeholders to make enterprise data secure, explainable, reusable, and ready for agentic AI workflows.

Maintain and validate equity, fixed-income, derivative, valuation, benchmarking, and securities data across investment systems. Manage scheduled data processes, deliver data-quality signoffs, investigate exceptions, resolve issues, and support investment teams with ad hoc analysis. Identify workflow improvements that improve efficiency, mitigate risk, and enhance service levels. Collaborate with operational and engineering teams while managing multiple projects and stakeholder requests.

Own the technical direction and implementation of authentication, authorization, and on-behalf-of delegation for Redpanda’s agentic AI platform. Extend its OAuth authorization server and token vault, build auditable delegation systems, and federate with enterprise identity providers such as Okta, Entra, and Ping. Lead technical decisions across multiple engineering teams, track emerging agent identity standards, collaborate with security and product teams, mentor engineers, represent technical positions externally, and deliver production-hardened systems.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

The Senior Data Engineer will design and maintain streaming data pipelines, develop analytical databases, and ensure data system reliability while collaborating with teams across the organization.

Build and maintain BI dashboards, reporting tools, and automated data workflows. Analyze customer behavior, funnels, retention, engagement, and product adoption to deliver actionable insights across Product, Operations, Marketing, Finance, and Leadership. Use AI tools and APIs to improve analytics workflows, self-service exploration, data-quality checks, and insight generation while validating accuracy, privacy, and business impact.

Leads Colliers’ enterprise data engineering strategy and a team of data engineering managers and engineers. Oversees scalable multi-cloud data platforms, microservices, data mesh and fabric capabilities, AI/ML pipelines, GenAI workflows, analytics, and business intelligence solutions. Establishes DataOps, governance, security, CI/CD, and reliability standards while managing platform operations, incident response, service levels, technology roadmaps, and cross-functional partnerships.

Build and scale data platforms, ETL pipelines, data lakes, warehouses, feature stores, and orchestration tooling. Integrate clickstream, relational, and unstructured data using Databricks and AWS. Partner with data scientists, ML engineers, product managers, and business stakeholders to support analytics, predictive modeling, and data-driven decisions. Maintain production systems, automate testing and monitoring, and communicate technical roadmaps and challenges.

Develops machine learning, deep learning, and predictive models using large structured and unstructured datasets. Mines data, identifies trends, evaluates AI/ML solutions, and creates scalable analytics processes to support business decisions, customer experience, and productivity. Collaborates with product teams, analytics professionals, and senior stakeholders to provide recommendations, shape roadmaps, and address complex business problems while managing risk within banking policies and regulations.

Develops and supports ETL applications and data pipelines across legacy and modern platforms. Responsibilities include systems analysis, design, construction, testing, implementation, operational support, troubleshooting ETL processes and SQL workloads, performance tuning, and continuous improvement. The role requires expertise in ETL architecture, data warehousing, Unix scripting, Oracle or MySQL, application integration, and database development, with exposure to streaming technologies and modern languages such as Spark, Scala, Java, or Python.

Manage Revlon's ecommerce sales channels by maintaining bilingual product listings, brand pages, pricing, promotions, content, and merchandising. Optimize listings for SEO and user experience, analyze traffic, conversion, sales, and reviews, and prepare performance reports. Support Shopify and marketplace operations, troubleshoot platform issues, coordinate digital marketing campaigns, and maintain brand consistency across online channels.

Designs, develops, and supports scalable enterprise data warehouses, data lakes, cloud data platforms, and ETL/ELT pipelines. Responsibilities include SQL development, Snowflake and Databricks engineering, Spark and Python programming, data modeling, source-to-target mapping, testing, reconciliation, performance tuning, production support, and cloud modernization. The role translates business and technical requirements into reliable data solutions and collaborates with architects, analysts, application teams, and reporting stakeholders.