Top Data Jobs in Ottawa

Lead enterprise Data & AI transformation initiatives, managing the delivery of cloud-based analytics platforms while aligning business and technology objectives. Responsibilities include program governance, stakeholder management, and overseeing implementations of Microsoft Fabric.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

As a Data Annotation Specialist, you'll label, audit, and correct machine learning data while ensuring high accuracy in Japanese. You'll also evaluate responses and recommend optimizations based on project guidelines.

Build and maintain scalable, reliable data systems and pipelines, uphold SLOs, partner with Product/Sales/Finance, enable self-service analytics, contribute to data platform strategy, and improve engineering practices for testing, reliability, and documentation.

Lead Andela Academy, scaling enterprise learning solutions in AI and Cloud. Build relationships, oversee team delivery, and drive revenue growth with high client satisfaction.

Lead operational management and support of the enterprise data platform (primarily Snowflake), ensuring stability, scalability, observability, incident management, and migration from SQL Server. Manage data operations team, establish runbooks/SLAs, coordinate cross-functional readiness, drive continuous improvement, and ensure governance, compliance, and performance of data pipelines and platform services.

Perform full-stack on-chain and product analytics: query on-chain data, profile ecosystem actors, benchmark protocols, define KPIs, build dashboards, and translate findings into actionable briefs for strategy, BD, product, and growth teams.

Own and drive the architecture and roadmap of ClickUp's data platform. Build scalable, reliable data pipelines and AI/ML infrastructure using AWS serverless, Snowflake, dbt, and Terraform. Lead cross-team technical initiatives, optimize cloud costs, establish engineering standards (observability, testing, CI/CD), mentor senior engineers, and influence org-wide architecture decisions.

Design and lead scalable enterprise data architectures using PostgreSQL and graph databases. Define logical and physical data models, optimize performance, modernize databases, integrate relational and graph systems, support AI/analytics platforms, produce governance documentation, and guide engineering teams through implementation.

Design end-to-end cloud-native AI and data platform architectures enabling Digital Twin and Knowledge Graph solutions. Lead architecture discussions, define scalable platform patterns, collaborate with AI/data/infrastructure teams, evaluate emerging AI technologies, produce technical documentation, and support solution discovery and roadmap planning.

Lead global data and AI engineering to deliver scalable, secure, and compliant data platforms and MLOps capabilities. Own strategy, architecture, operations, SRE practices, observability, and reusable self-service components to support analytics, AI, and enterprise programs.

Own and operate the data plane: develop Envoy/WASM filters for deep packet inspection, deploy and manage EKS workloads with Helm/GitOps and CI/CD, integrate Redis sidecars, provide observability (Prometheus/Grafana/OpenTelemetry), ensure reliable audit log streaming, and author ADRs as the data plane evolves.

Design and analyze experiments and causal studies, build retention measurement and predictive models, develop scalable PySpark/SQL data pipelines, create dashboards, and partner with product and growth teams to improve user engagement and retention.

Take end-to-end ownership of backend features and SaaS services in a microservices architecture. Design, implement, deploy, and operate scalable, reliable Node.js services. Work with Redis Streams, MongoDB, Kubernetes, and ensure test coverage, performance tuning, and collaboration with cross-functional teams.

Design and lead enterprise graph database and semantic data architectures, define ontologies and knowledge graph structures, integrate graph systems with cloud platforms, support LLM and AI use cases, run discovery workshops, document architecture, and mentor engineering teams.

Design and optimize AI-ready data models and feature-ready datasets for ML and analytics. Build semantic/analytical layers, enable real-time pipelines, support MLOps, model high-volume time-series/IoT data, and implement data lineage, observability, and governance for lakehouse architectures.

Own go-to-market strategy and adoption for Stripe's data products. Develop positioning, messaging, competitive intelligence, launch plans, demand programs, and sales enablement. Translate customer and market insights into roadmap recommendations and measurable KPIs while partnering cross-functionally to drive commercialization and growth.

Perform end-to-end data conversions for CityView implementations: map source-to-target data, develop reusable conversion scripts, troubleshoot issues, coordinate with customers and project teams, and deliver tested conversions to test and production environments while following CityView methodology and timelines.

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

Evaluate and rank multimodal model outputs, stress-test and document failures, author and edit dataset prompts/responses, build and apply rubrics, perform high-quality annotations across text, image, and structured formats, participate in calibration checks, adapt to evolving tasks, and report model performance trends to cross-functional teams.

Implement and configure enterprise Workday HCM solutions, lead the data workstream including gathering, transforming and loading organizational and employee data, liaise with customers and internal stakeholders, deliver data solutions, support sales and practice improvements, and educate others within the Workday practice.

Design, build, and operate scalable cloud-native data platforms and production-grade pipelines using Azure, Microsoft Fabric, and Databricks. Implement lakehouse/medallion architectures, transform data with SQL/PySpark/Python, enable BI and AI workloads, enforce data quality, governance and security, automate deployments with DataOps/IaC, optimize costs, produce runbooks/docs, and mentor junior data engineers.

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.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

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