Top Data Jobs in Ottawa

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.

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 and coordinate security projects and operations for the Corporate Security Office, supporting project lifecycle from planning to close-out. Provide data analysis, site inspections, hardware/software testing, security screenings, and policy/maintenance plan development while coordinating stakeholders and technical teams.

Provide on-site configuration and data management support for NASA AFRC projects: maintain CM plans and repositories, control configuration items and engineering changes, administer CCBs and ACMS/ACCT, run audits (PCAs), track action items and metrics, and train users on CM tools and processes.

CommBank is seeking Indigenous job seekers for various roles across its sectors, offering traineeships, internships, and direct employment opportunities with support from an Indigenous Careers team.

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.

Lead a hybrid engineering and data platform team to build scalable software and data infrastructure. Oversee Power Platform deployments, data architecture, governance and compliance (GDPR/HIPAA), BI/analytics, DevOps automation, and integration of AI/LLM and agentic solutions to drive intelligent automation and platform scalability.

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.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Intern will build enterprise data foundations and AI agents connecting LLMs to authoritative business knowledge. Responsibilities include developing retrieval/RAG pipelines, agent workflows, persistent memory/state management, governance controls, auditability, and integrations with vector DBs, knowledge graphs, enterprise tools, and multiple LLM providers.

Design, build, and scale Snowflake ingestion pipelines and ML/LLM systems. Create agentic tooling to generate, test, and maintain pipelines. Collaborate with analysts, data scientists, and product teams to deliver reliable, high-performance data infrastructure and operationalize models for an AI-first e-commerce platform.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

Drive technical pre-sales for Microsoft cloud database and analytics solutions via demos, PoCs, hackathons, and architecture workshops. Design secure, scalable data architectures, resolve technical blockers, collaborate with engineering, and evangelize Microsoft Fabric, Azure Databases, Databricks, Purview and EDW migrations to accelerate customer adoption.

Lead design, development, and optimization of enterprise data solutions. Build and maintain data pipelines, warehouses, and analytics platforms. Apply data governance, quality, metadata, and lineage practices. Ensure data security and compliance, monitor platform performance, and collaborate with stakeholders while providing technical guidance and mentorship.

In this role, you will define and execute the go-to-market strategy, drive growth, and lead customer success efforts at Reflow, a workplace intelligence platform.

As a Product Manager at Reflow, you will lead product strategy, execution, and delivery while ensuring customer engagement and managing engineering dependencies in an early-stage startup environment.

The Director of Product Management will lead product strategy across infrastructure, software enablement, and customer-facing platforms, enhancing both technical and user experience while managing a team of product managers.

Design and maintain enterprise semantic models and AI-ready data products, build and optimize Power BI datasets and DAX metrics, enforce governance and security, enable self-service and AI-assisted analytics, and collaborate with stakeholders and data engineers to support BI and ML integrations.

Design, build, and operate large-scale security data pipelines, lake/lakehouse storage, and analytics to normalize telemetry from a massive GPU fleet. Ensure data platform security (encryption, RBAC/ABAC, audit), enable trustworthy analytics and detection, collaborate across teams to define data contracts, and maintain data quality, provenance, and freshness for security posture and automated response.

Analyze large datasets to produce dashboards, metrics, and insights supporting product, care, and business decisions. Build BI infrastructure, run A/B and multivariate tests, perform ad-hoc analyses and visualizations, and collaborate with stakeholders and data scientists to inform strategy and operations.

Design, build, and maintain scalable data pipelines, optimize Databricks/Spark and Delta Lake architectures, deliver trusted datasets for analytics and ML, implement observability and data quality, and drive platform architecture and team mentorship.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

The Product Data Analyst will analyze operational data, develop analytics frameworks, create dashboards in Tableau, ensure data integrity, and collaborate across teams to enhance decision-making and reporting.

Design and operate distributed inference systems for LLMs, build large-scale data pipelines, and debug production issues while collaborating with researchers and ML engineers.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.