Top Data Jobs in Ottawa

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Own and evolve data infrastructure, distributed pipelines, data modeling, analytics, governance, semantic layers, and customer-facing data products. Operate cloud data systems, support production reliability and on-call incident response, and develop streaming and knowledge-management capabilities. The role also evaluates platform tools, manages stakeholder needs, and contributes to platform design and zero-to-one data system development.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Build and productionize statistical and ML models for Toast's restaurant platform. Partner with engineers, product, and business teams to mine data, design experiments, evaluate models, and communicate actionable insights that drive product growth and customer outcomes.

Owns end-to-end data infrastructure, including secure data lakes, streaming applications, data warehouse systems, replication and change data capture pipelines. Responsibilities include architecture, deployment, maintenance, performance optimization, security controls, documentation, operational runbooks, and cross-functional partnership with analytics and product teams. The role requires strategic data infrastructure thinking and production experience in SaaS or data-heavy environments.

Provides technical leadership for applied AI and data science initiatives from experimentation through production. Builds machine learning and generative AI solutions for anomaly detection, malicious behavior detection, fraud analysis, and security use cases. Designs LLM, retrieval, embedding, and agentic workflows; establishes evaluation and monitoring practices; partners with engineering and MLOps on reliable deployment; advises stakeholders; mentors technical contributors; and promotes secure, scalable, and responsible AI practices.

Lead applied AI projects from prototype to production, building and validating ML and GenAI models (LLMs, embeddings, RAG, agentic workflows) for malicious-behavior, anomaly, and fraud detection. Advise product and engineering teams, design experiments and evaluation pipelines, translate research into scalable APIs/workflows, and ensure data governance, privacy, and ethical practices.

Analyze and consolidate data from multiple systems, build scalable dashboards and executive reporting, define standardized metrics, ensure data quality, and translate complex datasets into actionable business insights. Partner cross-functionally with Sales, Marketing, Customer Success, Product, Finance, Operations, and Data Engineering to support decision-making and improve reporting processes.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, forecasting, targeting, and personalization. Collaborate with engineers and product teams, set modeling and productionization standards, mentor data scientists, guide architecture and experimentation, and apply cloud and MLOps best practices to drive measurable product and business impact.

Lead end-to-end data science initiatives: design, deploy, and monitor ML/statistical models for credit risk, pricing, collections and fraud. Collaborate cross-functionally to integrate models into production, define model development standards, mentor team members, and maintain scalable ML pipelines and monitoring systems to drive measurable business impact.

Build and operate ETL and real-time pipelines connecting veterinary practice systems, billing platforms, and third-party APIs. Design data models and a warehouse supporting hospital analytics, finance, and reporting. Establish standards for testing, documentation, monitoring, and data quality, while troubleshooting production data issues. Partner with Finance to improve Sigma self-service reporting and define scalable integration patterns as the company’s first data engineer.

Analyzes pipeline, funnel conversion, campaign performance, retention, and revenue drivers across Sales, Marketing, and Client Success. Builds and maintains dbt models, data marts, quality controls, documentation, and BI-as-code assets using Salesforce and modern data-stack technologies. Partners with stakeholders to turn ambiguous questions into decisions and advances AI-agent workflows for faster analytics and reporting.

Leads data engineering consulting engagements, serving as a subject matter expert and data architect. Designs scalable data environments, models, pipelines, integrations, warehouses, and visualizations while ensuring quality and governance. Leads architectural decisions, project delivery, technology strategy, and AI-augmented development practices. Mentors technical teams, collaborates with data scientists, analysts, clients, and stakeholders, and drives process improvements and innovation. The remote role may require infrequent travel to the United States.

Build reusable Python libraries and reproducible analysis pipelines for image and experimental data; define data models and provenance; partner with wet‑lab scientists to translate requirements and validate methods; establish testing, validation, versioning, and observability for scientific workflows.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Leads sales strategy, market expansion, enterprise deal execution, and team management for private markets software and data solutions across the Americas. Builds and manages sales, business development, and presales teams; owns pipeline performance, pricing, forecasting, and major client relationships. Personally closes complex enterprise agreements with financial institutions, collaborates with product, professional services, and customer success teams, and represents the company at industry events.

Own the data platform product roadmap, including multi-chain ingestion, on-chain parsing, token and market data, APIs, datasets, analytics tools, governance, quality monitoring, and metric frameworks. Partner with engineering, backend, algorithm, BI, risk, growth, and business teams to deliver reusable data products. The role requires substantial product management experience, hands-on Web3 knowledge, understanding of blockchain data models, SQL proficiency, and familiarity with data infrastructure and analytics tooling.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.

Design, build, and operate scalable data ingestion, replication, and lakehouse infrastructure to move petabytes of data into a Delta Lake on S3. Improve reliability, observability, security, and developer experience for Spark/Databricks processing. Develop internal libraries and tooling (Go/Python), collaborate with cross-functional teams, and help shape long-term data platform and AI-ready infrastructure.

Design, build, and operate large-scale Spark/PySpark data pipelines and the enterprise data platform. Implement data integrations, transformations (Python/SQL), monitoring, and data quality. Build MCP servers and AI-enabled tooling, optimize performance, mentor engineers, and lead cross-functional data projects to enable analytics and automation.

Lead and grow a data engineering team to build and operate ingestion, transformation, orchestration, and serving layers. Drive data quality, observability, cost efficiency, self-serve analytics, engineering best practices, and cross-functional partnerships to deliver reliable, reusable data products for product, GTM, finance, and CTO stakeholders.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

Coordinate customer onboarding, training, support, and retention workflows. Follow up on customer issues, align Sales, Accounting, Software, integrations, and licensing teams, and drive commitments through resolution. Build and improve operational processes, track projects, dependencies, blockers, and next steps, identify improvement opportunities from feedback, and provide clear updates across teams.