Top Data Jobs in Ottawa

Lead and coordinate client legacy data integration into TELUS Health systems. Oversee conversion tasks, ensure data quality, run workshops, identify gaps, propose process changes, escalate issues, prioritize conversion work, and report progress and risks to project management.

Design, build, and deploy ML and AI systems at scale for content classification, recommendations, search, and metadata enrichment. Work with cross-functional teams to process large datasets, develop models from classical NLP to LLMs, implement production pipelines, and communicate results to stakeholders.

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.

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.

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.

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.

Seeking a Senior Data Scientist to lead predictive analytics and machine learning initiatives. Responsibilities include building ML models, data management, and mentoring junior data scientists. Must have expertise in AWS and strong statistical knowledge.

Lead and manage the strategy, development, and operation of BECU's Enterprise Data Platform, ensuring secure and scalable data capabilities while fostering team collaboration and data governance.

As a Senior Data Scientist in Marketing Analytics, you'll partner with Marketing to solve problems, design data-driven solutions, lead experimentation, communicate insights, and operationalize solutions for business impact.

Build scalable ML data pipelines for Waabi's autonomous driving platform. Design, optimize, and manage datasets and training processes while collaborating with scientists and engineers.

As a Data Scientist in Product Analytics, you will analyze large datasets, improve user experience, conduct A/B tests, and collaborate with teams to drive product insights and customer engagement.

Design and analyze large-scale A/B and causal studies, build predictive and decision models, apply learning analytics and psychometric methods to measure learner outcomes, partner with product and engineering to instrument data and drive product decisions.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Design, build, and maintain scalable Databricks-based ETL/ELT pipelines and data models optimized for AI/ML. Manage orchestration, data quality, governance, and CI/CD (ADO → GitHub) while collaborating with cross-functional teams in Agile delivery.

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.