Top Data Jobs in Ottawa

Leads complex qualitative and quantitative public health analyses, including data integration, cleaning, validation, mixed-methods evaluation, and cross-site comparisons. Develops reproducible analytic processes, structured datasets, data-quality documentation, technical reports, and recommendations for program improvement. Supports CDC administrative systems, performance measurement, implementation evaluation, and communication of findings to senior stakeholders and public health organizations.

Designs, develops, tests, and maintains cloud-based data pipelines and warehouses for large-scale datasets. The role involves data ingestion using Python and SQL, GCP services, production migrations, infrastructure troubleshooting, and optimization. The engineer collaborates with stakeholders to understand project goals and applies AI/LLM tools thoughtfully within data engineering workflows. Responsibilities also include distributed data manipulation, technical guidance, and maintaining scalable, cost-efficient data architecture.

Leads Medavie’s enterprise data, analytics, and AI strategy and program governance. Defines the multi-year roadmap, strategic guardrails, portfolio priorities, governance forums, decision rights, reporting, metrics, and value-realization practices. Partners with executives, business lines, IT, risk, privacy, and legal teams to align investments, manage strategic risks, promote responsible AI, and advance data and AI literacy across the organization.

Own the vision, strategy, roadmap, development, commercialization, and lifecycle of commercial data products. Identify customer and market opportunities, translate needs into product requirements, and collaborate with data engineering, data science, analytics, software, sales, and marketing teams. Lead discovery, launches, monetization, adoption, optimization, and measurement of business outcomes across revenue, operations, customer experience, and decision-making.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

Owns data stewardship for corporate transactions and regulatory matters, ensuring accurate, consistent, secure data across firm systems. Responsibilities include designing metadata models, resolving data quality issues, auditing dashboards, supporting migrations, verifying transactional information with attorneys, organizing data for due diligence, and assisting with governance policies. The role collaborates with knowledge management, information governance, records management, attorneys, and business services teams.

Leads a team delivering enterprise data engineering solutions, overseeing pipeline design, development, testing, deployment, operations, and reliability. Owns ETL and ELT modernization on cloud platforms, data architecture, solution design, stakeholder collaboration, vendor delivery, governance, security, monitoring, and technical documentation. Translates business requirements into curated data products while managing project scope, budgets, risks, and timelines. Coaches data engineers, analysts, and business systems analysts and drives continuous improvement.

Leads presales activities for insurance software and geospatial analytics solutions, including customer discovery, requirements analysis, personalized demonstrations, presentations, proposals, RFP/RFI responses, and competitive materials. Partners with sales, product marketing, cloud operations, and professional services teams. Advises customers on cloud migration and integration requirements, supports implementation transitions, and presents at industry events. Requires substantial insurance-sector expertise, strong communication skills, AI enablement experience, and travel up to 50%.

Owns advanced reporting, dashboards, data analysis, KPI development, ETL requirements, and data quality initiatives for internal stakeholders. The role delivers recurring and ad hoc reports, builds automated workflows, documents data definitions and processes, communicates insights to senior leaders, and supports BI sprint planning. Strong SQL, data visualization, stakeholder communication, and systematic data quality problem-solving are required.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Lead event-driven and batch processing decisions, establish standards for observability, freshness, and failure handling, and guide cross-functional platform design. Mentor engineers, conduct design reviews, influence teams without direct authority, and drive foundational platform improvements. The role requires deep Python and SQL expertise, cloud data warehouse experience, scalable API-based ETL design, orchestration, and containerized infrastructure experience.

Build and maintain reliable data pipelines, integrations, APIs, and event-driven or batch processing systems. Transform data from advertising, commerce, and order management platforms while improving monitoring, data freshness, performance, security, and scalability. Partner with product, infrastructure, and data engineering teams to expand the integration platform and onboard new sources in a cloud-native, multi-tenant environment.

Process client records in a fast-paced warehouse environment by sorting and categorizing files, creating labels, entering high volumes of record numbers, meeting production targets, and maintaining accuracy and compliance. The role requires strong English communication, 10-key proficiency, attention to detail, and the ability to stand for extended periods and lift boxes weighing up to 80–90 pounds.

Manages data entry, repositories, quality checks, documentation, governance, reporting, and controlled data distribution across multiple projects. The role supports data tools, extracts, user onboarding, troubleshooting, and process improvement while ensuring accuracy, security, and compliance. It requires strong organization, analytical ability, attention to detail, and collaboration across teams. Experience with data management and Windchill PLM is valuable, particularly in engineering or defense environments.

Leads HR data governance, compliance, payroll taxation compliance, position management, and national HR administration. Drives process transformation and automation using Workday, Power BI, Power Automate, and related tools. Establishes data standards, controls, audit readiness, service measures, and quality improvements while partnering across HR, Technology, Finance, and business teams. Manages and develops teams, leads organizational change, and ensures compliance with Canadian employment, privacy, payroll, and internal policy requirements.

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.