Top Data Jobs in Ottawa

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.

The Senior Staff Software Engineer will architect Affirm's lakehouse analytics platform, implement data governance, mentor engineers, and collaborate cross-functionally to optimize data infrastructure and solutions.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Analyze and document business and system requirements for an Intelligence Mission Data project; facilitate workshops and use-case development; produce system specifications and traceability; perform technical options analysis and solution evaluations; plan organizational impact and integration; participate in testing; and prepare briefings, reports, and meeting artifacts.

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.

Lead Andela Academy, scaling enterprise learning solutions in AI and Cloud. Build relationships, oversee team delivery, and drive revenue growth with high client satisfaction.

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.

Embed in a core business area to define problems, build metrics/reports, run analyses and experiments, and drive data-informed product, growth, AI, or partnership outcomes end-to-end.

Embedded on a core business team, the Associate Data Scientist defines problems, builds metrics/reports, runs analyses and experiments, surfaces insights, and drives data-informed recommendations end-to-end to impact growth, product, AI/automation, or partnerships.

Embed with a core business area to define right problems, build metrics, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end to improve product, growth, AI, or partnerships.

Embedded within a business unit, the Data Scientist defines the right problems, builds metrics, analyses, reports, and experiments, and delivers data-driven recommendations and end-to-end execution to improve product, growth, AI/automation, or partnerships.

Label, rank, audit, and refine human- and model-generated text to improve LLM safety and policy alignment. Apply nuanced safety guidelines, create adversarial prompts and test cases, identify annotation inconsistencies, and provide feedback. Work independently in a remote, asynchronous team, handling potentially explicit or disturbing content with high attention to detail and consistency.

Work embedded in a business unit to define problems, run analyses, build reports and experiments, surface insights, and drive data-informed decisions end-to-end with product, growth, AI, or partnerships teams.

Embedded within a core business area, you will define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end to impact growth, product, AI/automation, or partnerships.

Embedded within a core business area, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, recommend actions, and drive outcomes with stakeholders.

Embed within a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive data-informed recommendations end-to-end with product, growth, and operations.

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.

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.

Build, operate, and own high-scale backend services focused on networking, performance, reliability, and security. Design systems, implement features, troubleshoot protocol-level networking issues, write maintainable tests, and collaborate across teams.

Analyze and operationalize consent and privacy-related data across its lifecycle. Use SQL for data profiling and anomaly detection, design consent-focused data models, support OneTrust and integrations with CRM/ESPs, enforce data governance and GDPR/CCPA compliance, monitor data quality, document lineage, and collaborate cross-functionally in Agile teams.

Lead analytics for Internal Audit Claims: extract, transform, reconcile, and validate large claims datasets; build and maintain Power BI reports/dashboards; perform QA and tie-outs; support automation, bots/AI enablement, and executive reporting; mentor other analysts and partner with audit, IT, and business stakeholders to deliver repeatable, validated analytics.

Lead end-to-end data engineering projects for a Canadian financial institution, liaise with stakeholders to gather requirements, provide technical leadership and architecture guidance, ensure delivery quality and documentation, and work independently to identify and resolve blockers.

Lead end-to-end delivery of enterprise data platform initiatives, partner with stakeholders to define requirements, own quality and documentation, mentor peers, drive technical decisions, and represent engineering in cross-functional discussions for regulated environments.

Design and optimize AI-ready data models and feature-ready datasets for ML and analytics. Build semantic/analytical layers, enable real-time pipelines, support MLOps, model high-volume time-series/IoT data, and implement data lineage, observability, and governance for lakehouse architectures.