Top Data Jobs in Ottawa

Design and own end-to-end Azure-based data platform architecture using Databricks. Define ingestion, transformation, governance (Unity Catalog), and delivery standards; optimize performance and cost; integrate with Azure ecosystem and Power BI; mentor teams and guide delivery and CI/CD practices.

Lead and grow a full‑stack engineering team building customer data and identity solutions. Own architecture, APIs, component libraries, code quality, hiring, mentoring, roadmap execution, and cross‑functional collaboration. Contribute code for high‑impact items and ensure scalable systems and agile best practices.

Design, build, deploy, and maintain production-grade measurement and ML systems (MMM, incrementality, insights, recommendations). Translate statistical and causal methods into scalable products, debug full-stack data and model issues, evaluate methodology tradeoffs, and collaborate with engineering, product, and customer teams while providing customer-facing technical support.

Lead platform and data strategy by defining microservice platform services, API contract and governance, Snowflake lakehouse direction, and data monetization. Partner with architecture, engineering, analytics, and commercial teams to drive platform performance, scalability, international readiness, and developer experience while influencing cross-functional teams without direct authority.

Lead and optimize data collection, validation, storage and reporting activities. Ensure data quality and governance, implement process improvements, support new systems rollout, mentor junior staff, and maintain documentation and compliance with security and regulatory requirements.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.

As a Staff Software Engineer, you'll architect and build foundational platform services, focusing on data infrastructure, healthcare interoperability, and secure APIs while mentoring other engineers.

Design business data models and data architectures, mentor data teams, translate business requirements into technical specifications, and set standards for AI-augmented development practices.

The Data Governance Intern will assist in developing enterprise data catalogs, documenting data elements, and supporting data quality efforts using SQL and Python.

The AI Data Expert will review and evaluate AI-generated outputs and textual content, ensuring cultural relevance and quality. Responsibilities include content analysis, assessing multimedia materials, and providing structured annotations while adhering to detailed guidelines. Native fluency in French and strong English skills are essential, with previous experience in AI evaluation preferred.

Evaluate and review AI outputs, generate or assess content, and ensure cultural relevance and accuracy in various media for AI projects.

Lead and scale the Growth engineering organization at MaintainX, focusing on technical strategy, product-led growth, and enhancing cross-functional initiatives.

Lead Data Acquisition Team, drive AI-augmented development, establish specification-driven methodology, oversee data pipeline architecture and ensure operational excellence.

The Container Optimization Marketing Manager will strategize and execute marketing initiatives to drive growth and pipeline for DoiT's Kubernetes solutions, ensuring competitive market positioning and measurable revenue impact.

Support GTM analytics by analyzing Sales, Revenue Operations, and Customer Success data; build and maintain dashboards and recurring reports; perform ad-hoc analyses and experimentation evaluation; define and maintain reliable metrics with analytics teammates; and document and communicate insights to technical and non-technical stakeholders.

Lead enterprise data architecture and modeling to consolidate legacy court reporting marts into a centralized EDW. Design data models, ETL processes, data governance, and Power BI reporting. Provide architecture alignment with GAEA, conduct risk assessments, mentor teams, and support migration and implementation activities.

Lead and scale a product-focused Data Science team to drive acquisition, engagement, retention, and member health outcomes. Set DS strategy, measurement frameworks, operating model, and prioritize high-impact analytics. Partner cross-functionally, ensure rigorous, actionable analyses, apply experimentation, causal inference, segmentation, forecasting and personalization, and collaborate with data engineering to improve data foundations.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

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.

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 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.

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

Audit and ensure quality of data labeling outputs, coach and onboard annotators, apply and refine guidelines, monitor workflows and escalate issues, document findings, and support human-in-the-loop AI evaluation processes.

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.