Top Data Jobs in Ottawa

As a Data Analyst, you will analyze user behavior, create interactive dashboards, and collaborate with teams to provide actionable insights for product improvement.

Analyze user behavior and product performance, create reports and dashboards, and provide data-driven insights to teams for decision-making.

The Staff Backend Engineer will architect high-throughput streaming systems, define technical direction, mentor engineers, and solve complex problems for Helius' core products.

Design and optimize SQL Server solutions, manage data, automate processes with PowerShell, and integrate with external systems to enhance functionality.

The Data Scientist II will work with sports tracking data to build models and analyses for NHL and NBA organizations, focusing on data transformation and metrics development.

The role involves designing and implementing scalable data models using Data Vault techniques on Azure. Responsibilities include collaborating with stakeholders, optimizing data vault models, ensuring data security, and documenting data processes.

As a Backend Engineer on the Data team, you will design and maintain data pipelines and services, ensuring data quality and scalability while collaborating with cross-functional teams to meet data needs.

The Guidewire Data Engineer will design schemas in Snowflake, develop ETL processes, optimize SQL queries, and integrate Guidewire with data platforms.

Lead a distributed team of data scientists and engineers, focusing on data infrastructure and ML systems while collaborating with product and marketing teams.

Own analytics enablement by translating stakeholder questions into trusted, reusable metrics and semantic models. Build and maintain the metrics/semantic layer, validation, documentation, AI-accessible datasets, and pipelines to ingest new sources. Enable self-serve analytics and agentic LLM querying (Claude) while partnering with product, GTM, and executives.

Design and maintain scalable ELT/ETL pipelines on GCP, build and evaluate predictive ML and NLP models (including LLM prompt engineering), implement XAI for interpretability, perform EDA and automation, and document governance and compliance to support enterprise fraud detection and SIU stakeholders.

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

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

Lead the PoC data science team, delivering POCs for enterprise clients, mentoring team members, and driving fraud analytics and ML solutions.

As an Intermediate Data Engineer, you will develop data infrastructure and products, collaborate with diverse teams, and enhance technical quality across the data team.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Evaluate AI outputs, audit model behavior, apply and refine labeling guidelines, identify failure modes and trends, document findings, and collaborate with cross-functional teams to improve model evaluation and data quality.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Evaluate AI model outputs, perform audit-based reviews, identify failure modes and trends, apply and improve guidelines, document findings, and collaborate with leads and cross-functional teams to refine quality standards.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.

Audit and improve data labeling quality in human-in-the-loop ML workflows. Coach and onboard labelers, provide actionable feedback, monitor queues, identify recurring issues, and help refine guidelines and processes.

Evaluate AI outputs and audit model behavior, apply and refine guidelines, identify failure modes and trends, document findings, and collaborate with leads and cross-functional teams to improve evaluation frameworks and data quality.