Top Data Jobs in Ottawa

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Analyze complex structured and unstructured datasets, develop predictive models, apply statistical and machine learning methods, identify data quality issues, and generate actionable insights. The role integrates data from multiple sources, supports modernization and governance initiatives, creates dashboards and analytical frameworks, and communicates findings to technical and non-technical stakeholders. It is hybrid in Toronto with onsite work required five days as project needs dictate.

Build and improve scalable full-stack products for higher education, owning initiatives across the software development lifecycle from discovery through release. Collaborate with Product, Design, and Engineering in a cross-functional Scrum team, experiment with AI technologies, resolve bugs, complete tickets, and deliver reliable, tested, user-focused features.

Manage interfaces and eligibility files for group insurance: perform daily data loading, validation and updates, document procedures, communicate with clients and stakeholders, and recommend process improvements and automation.

This role involves deploying solutions for clients, managing data compliance, and providing feedback for product improvement. Requires collaboration with teams and clients.

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.

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.

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.

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.

As a Data Analyst, you will analyze user behavior, create interactive dashboards, and collaborate with teams to provide actionable insights for product improvement.

Analyze user behavior and product performance, create reports and dashboards, and provide data-driven insights to teams for decision-making.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

The Guidewire Data Engineer will design schemas in Snowflake, develop ETL processes, optimize SQL queries, and integrate Guidewire with data platforms.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from ad platforms, order systems, and real-time events. Ensure reliability, observability, and maintainability across event-driven and batch architectures, implement authentication flows, optimize data workflows in a cloud-native environment, and collaborate with product, infrastructure, and data teams to onboard new sources.

Data Contributors will research startups, analyze global data for StartupBlink's Global Startup Ecosystem Index, and compile accurate information for publication.

Design and optimize SQL Server solutions, manage data, automate processes with PowerShell, and integrate with external systems to enhance functionality.

Lead design and implementation of Jane's customer data infrastructure: own event collection, pipelines, CDP tooling, schema governance, identity resolution, and attribution. Work cross-functionally with marketing, BI, product, and engineering to set standards, evaluate tooling (e.g., RudderStack), ensure privacy-compliant instrumentation, and build reliable, consistent data delivery to the warehouse.

Lead a distributed team of data scientists and engineers, focusing on data infrastructure and ML systems while collaborating with product and marketing teams.

As a Data Analyst at Stripe, you'll build data products, metrics, and dashboards, analyze large datasets, and communicate insights to drive business decisions.

Contributors record authentic first-person video of household tasks, hands-on work, and everyday activities using a phone mounted to a headstrap. They choose their own schedule, preferably recording at least 10 hours weekly, then upload footage through Perle’s app for review and payment. Contributors must be at least 18, reside in the United States, Canada, or Latin America, have a supported smartphone, and be comfortable wearing a headstrap.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.