Top Data Jobs in Ottawa

Lead and scale Scribd's Data & Analytics organization across Product Analytics, Data Science, and Analytics Engineering. Build operating model, data governance, experimentation frameworks, AI-native workflows, and infrastructure to accelerate insight delivery. Develop director-level leaders, recruit talent, and embed data-driven decision-making at the executive level to drive growth, retention, and portfolio-wide strategy.

Lead AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.

The Staff Data Product Manager will unify data architecture, lead product strategy for AI-powered data products, and ensure rapid delivery of innovative solutions for real estate agents. This role emphasizes data product management, AI capabilities, and deep understanding of real estate data compliance.

Design and deploy predictive models and segmentations using statistical and ML/AI methods. Prepare, clean, and feature-engineer data from warehouses, develop and operationalize models, create visualizations and presentations, document rules, and advise stakeholders to inform business decisions and improve data management tools.

The Senior Data Engineer designs and supports databases for an IVR platform, ensuring performance and scalability while collaborating with multiple teams on data availability and integrity.

Design, build, and maintain scalable batch and streaming data pipelines and domain-oriented data models using dbt and cloud warehouses. Ensure data quality, documentation, and usability for analytics, ML, and reporting while collaborating with Product, Analytics, and ML teams.

Design, build, and maintain robust terabyte-scale data pipelines and stream-processing applications. Collaborate with product teams, participate in full SDLC, automate tests and deployments, monitor operations, and contribute to technical strategy and continuous improvement. Participate in on-call rotation.

The Senior Data Platform Engineer will manage and develop the data infrastructure on Databricks and AWS, ensuring scalable and efficient data capabilities while collaborating across teams.

The Staff Software Engineer will lead the development of backend services and data pipelines for real estate MLS data, mentor teams, and work on AI integration.

This role involves deploying solutions for clients, managing data compliance, and providing feedback for product improvement. Requires collaboration with teams and clients.

The role involves defining and executing the marketing strategy for Stripe's Tax and Data products, leading a team, communicating cross-functionally, and using data insights to drive growth.

Lead design and implement a scalable Medallion lakehouse on GCS and Databricks. Build and govern ingestion and transformation pipelines (Astro/Airflow, PySpark, Delta Live Tables), apply domain-driven data modeling, manage Unity Catalog access and lineage, collaborate with analytics and vendors, and proactively surface risks with clear documentation.

The Data Reliability Engineer will enhance the resilience and scalability of data infrastructure, focusing on automation and reliability. Responsibilities include managing data pipelines, operating Kubernetes clusters, and defining observability standards.

As a Data Annotation Specialist, you will label, audit, and optimize data for AI models in Chinese while ensuring high accuracy and quality. You will perform repetitive tasks and collaborate with teams, providing feedback and recommendations for data improvement.

As a Software Engineer, you will create tools and pipelines for AI development, manage data solutions for ML teams, and ensure high-quality code.

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.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Embed with a core business unit to define problems, build metrics, run analyses and experiments, surface insights, and drive data-backed recommendations end-to-end to improve product, growth, AI/automation, or partnerships.

Embedded in a core business area, own end-to-end analytics: define problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-informed recommendations to improve product, growth, AI, and partnership outcomes.

Embedded on a core business team, own end-to-end analytics: define problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-informed decisions with stakeholders to improve product, growth, AI/automation, or partnerships.

Own the analytics function embedded in a business unit: define problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end to impact growth, product, AI/automation, or partnerships.

Embedded on a core business team, you will define analytical problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed recommendations end-to-end to improve product, growth, AI, or partnerships.

Embedded on a core business team, the Data Scientist will define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embed with a core business unit to define analytical problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end to impact growth, product, AI, or partnerships.

Lead embedded data science for a core business area (growth, product, AI/automation, or partnerships). Define problems, build metrics and analyses, run experiments, surface insights, make recommendations, and drive execution cross-functionally to improve key business metrics.