Top Data Jobs in Ottawa

Own the zero-to-one lifecycle for integrating structured financial data into AlphaSense. Translate investor research and capital-markets workflows into data requirements, architectures, and product solutions. Partner with engineering and platform teams across Web, Excel, and AI to deliver, test, launch, and enhance products. Conduct customer research, analyze competitors and industry trends, prioritize trade-offs, and align cross-functional stakeholders around product strategy and execution.

Lead predictive modeling and causal inference initiatives for clients, developing models from exploratory analysis through production deployment, monitoring, and retraining. Apply GLMs, econometric methods, uplift modeling, propensity methods, and deep-learning forecasting. Process large-scale data with SQL and Spark, build models in Python, improve production reliability, and communicate findings to technical and business stakeholders in a consulting environment.

Conduct real-world data analyses using SAS or R and commercial claims and EHR databases. Build longitudinal study cohorts, define baseline and outcome variables, apply epidemiologic methods, perform statistical tests, regression and survival analyses, assess data quality, review protocols and statistical analysis plans, and program tables, figures, and listings. The role requires advanced biostatistics or epidemiology expertise and strong communication skills.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Analyze go-to-market performance across marketing, sales, revenue operations, and finance. Build reporting connecting campaigns to pipeline and revenue, investigate funnel and attribution questions, support budget analysis and planning, improve data quality, and communicate insights to technical and non-technical stakeholders. Collaborate with AI and Data Engineering teams on marketing data agents, architecture, and source tables.

Build and deploy LLM agents, Python automations, data workflows, dashboards, and root-cause analysis tools. Analyze telecommunications data with SQL to improve network quality, reduce fraud and billing errors, and optimize margins. Establish data architecture, governance, KPIs, and safe AI orchestration frameworks. Partner cross-functionally with Carrier Relations, Technology, Product, Supply Operations, and Finance to deliver scalable data products and communicate actionable insights.

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.

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.

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.

Lead technical strategy and hands-on implementation for Faire's data platform. Architect and scale ETL/ELT, streaming and batch pipelines, data warehousing, orchestration, monitoring, cost optimization, security, and governance. Mentor teams, prioritize platform roadmaps, and partner with product, analytics, and data science to enable self-serve data capabilities across the company.

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 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.

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.

Leads donor database and fundraising systems management, data integrity, gift processing, integrations, reporting, segmentation, automation, compliance, and process improvement. Manages and coaches a Database Coordinator while partnering with fundraising, communications, finance, and global teams. Develops Power BI dashboards, analyzes donor and campaign data, oversees vendor relationships, maintains data quality, and translates complex information into actionable fundraising and organizational insights.

Designs, builds, and maintains scalable batch and real-time data pipelines supporting an IoT safety ecosystem. The role manages ETL/ELT processes, data lakes, warehouses, lakehouses, orchestration, monitoring, data quality, and observability. It partners with product, engineering, analytics, and data science teams to deliver reliable data services, productionize models, and shape long-term data platform architecture using AWS and modern infrastructure practices.

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.

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.

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.

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.

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 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.