Top Data Jobs in Ottawa

Lead full-cycle enterprise sales for data and intelligence consulting: originate pipeline, run discovery, manage complex multi-stakeholder deals, create SOWs with delivery teams, co-sell with partners, and meet revenue targets while leveraging AI tools.

The Staff Data Scientist will own the analytical strategy for Firefox, driving data-informed decisions through measurement frameworks, experiments, and user growth analysis, while mentoring other data scientists.

The Staff Data Scientist will lead analytical strategies for Firefox, focusing on product growth, measurement frameworks, and mentoring data scientists while guiding data-driven decisions.

Abstract and enter clinical data from healthcare records, apply clinical operations policies, troubleshoot VPN/MFA access, maintain confidentiality, and provide guidance to less-experienced team members while ensuring data quality and workflow adherence.

Lead and build Burq's data and ML platform: own warehouse, ingestion, semantic layer, and BI; architect and ship forecasting, optimization, matching, and reliability models; implement feature pipelines, training/serving, and monitoring; drive AI agent initiatives; hire and grow a data/ML team; set governance, intake processes, and partner with product, engineering, and commercial stakeholders to align models with business impact.

The GTM Lead for Insurance will build and manage enterprise insurance accounts, develop relationships with senior industry leaders, and shape sales strategies for AI solutions.

Lead analytics and operational insights for Customer Operations: define core metrics, build dashboards and forecasts, model churn/renewal risk, enable self-serve reporting, implement AI-enabled automation, and partner cross-functionally to drive retention, expansion, and support efficiency.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Perform product and player-behavior analysis for Marvel Strike Force using SQL/Python and reporting tools. Build recurring KPI reports, design/validate analytics tracking and experiments, partner with cross-functional teams, and use AI-enabled tools to accelerate workflows while ensuring data quality, privacy, and clear executive-ready insights.

Lead product execution for retail platforms, integrations, and data foundations. Establish data governance, define integration patterns and data contracts, improve fulfillment/OMS capabilities, evaluate vendors, and enable analytics and AI-driven automation to increase operational efficiency.

Manage technical data for IP Routing hardware NPI programs: support NPI from data entry through factory implementation, maintain product data and Engineering Change Orders across the product lifecycle, and coordinate with engineering, supply chain, and manufacturing to ensure accurate PLM/PDM records and implementation.

The Data Migration Specialist will manage end-to-end data migrations, ensuring data quality and effective communication with clients and internal teams while resolving challenges.

The Forward Deployed Marketing Data Scientist will work with AI customers and engineering teams to optimize AI-driven marketing campaigns through data analysis, diagnostics, and communication of insights to stakeholders.

As a Data Engineer, you'll design, build, and maintain data pipelines, ensure data quality, and collaborate with stakeholders to deliver data solutions.

Label and review 2D images and 3D sensor (LiDAR) data using annotation tools to create bounding boxes, cuboids, and other shapes. Ensure annotations follow guidelines, correct or reject pre-labeled data, provide notes, learn internal tools, and collaborate to improve data quality for autonomous vehicle AI.

Build and own a versioned, documented gold data layer from lakehouse silver tables for AI research. Transform, validate, and productize multi-modal datasets and pipelines (Databricks/PySpark) to support model development, evaluation, and production ML/LLM workflows while ensuring data quality, provenance, and regulatory controls.

Lead and grow a small data team while remaining hands-on in data engineering, analytics engineering, and infrastructure. Own data strategy, warehouse architecture, pipelines, modeling, and self-serve analytics. Partner cross-functionally to deliver trusted metrics, experimentation, and applied ML capabilities (search, recommendations, personalization). Drive adoption of data quality, documentation, self-service, and modern data practices.

The Data Product Manager defines the reporting roadmap, manages KPI alignment, collaborates cross-functionally, ensures data quality, and leads continuous improvement in analytics processes.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.

Build and maintain the product analytics data foundation: design scalable dbt models, define semantic models and governed metrics, partner with Engineering on instrumentation, certify datasets for BI tools, monitor pipelines and data quality, and use AI to accelerate analytics engineering workflows.

Own and operate backend and data services for a cybersecurity platform: design and run high-volume streaming ingestion, ETL/ELT pipelines, lakehouse/warehouse modeling, APIs, and observability. Mentor engineers, contribute across TypeScript/Python/Go microservices on Kubernetes, ensure performance/scale/security, and evaluate AI tools to accelerate development.

Design, build, and maintain scalable cloud data pipelines and architectures. Develop and optimize ETL/ELT workflows, manage data warehouses (primarily BigQuery), implement governance and security, collaborate with engineers and analytics teams, enable ML/AI use cases, and maintain documentation, lineage, and high-availability pipelines.

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.

Own and scale end-to-end physical-data collection operations: scheduling, operator utilization, station uptime, throughput and quality. Recruit and develop operators and shift leads, standardize playbooks and SOPs across sites, partner with Quality and Annotation teams, stand up partner-site deployments, and build reporting that tracks collected hours, quality, and cost.