Top Data Jobs in Ottawa

Provides presales technical expertise for HPE storage solutions, including solution architecture, proposals, demonstrations, presentations, training, and customer migrations or integrations. Supports sales specialists and channel partners, maintains customer relationships, achieves sales KPIs and quotas, and advises on data lifecycle management, virtualization, application stacks, public cloud, backup, and ransomware protection. Requires deep storage-industry expertise and competitive knowledge.

Leads strategic pricing and monetization across sales-driven channels, managing pricing partners and advising on complex commercial deals. Develops pricing frameworks, approval processes, deal governance, and scalable tools using automation, AI, and data. Partners with Sales, Product, Engineering, Legal, Risk, and Revenue Operations to improve deal economics, profitability, pricing performance, packaging, and portfolio-level monetization strategy.

Lead presales architecture engagements by conducting technical discovery, translating business needs into scalable technology solutions, developing proposals and estimates, supporting sales opportunities, and coordinating transitions into delivery. The role spans cloud, infrastructure modernization, platform engineering, containerization, cybersecurity, data, and AI. It requires client-facing communication, technical solution design, delivery estimation, partner collaboration, and the ability to align technical recommendations with business outcomes and commercial expectations.

Build a high-performance frontend data platform for exploring and analyzing hardware telemetry. Develop React and TypeScript applications, interactive time-series visualizations, data visualization plugins, complex application state management, and LLM-assisted analysis workflows. Collaborate directly with hardware engineers and customers to define interfaces, prototype workflows, demonstrate features, and incorporate feedback into product development.

Leads Databricks’ Public Sector enterprise sales organization in Ottawa, managing account executives, quotas, territory planning, forecasting, hiring, coaching, and revenue growth. Builds executive customer and partner relationships, drives consumption- and commitment-based sales, ensures forecast accuracy, and collaborates with cross-functional leaders to execute strategic growth plans. Requires leadership experience in enterprise software sales across data, AI, cloud, or SaaS, preferably serving public-sector customers.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

Build and optimize scalable ETL/ELT pipelines, data integrations, and analytical datasets for Ericsson’s CRAnE platform. Improve data quality, lineage, reliability, and automation while translating stakeholder needs into robust data solutions. Contribute to platform modernization, proofs of concept, and production delivery supporting Radio and Compute product development.

Develop and own scalable AI-enabled software and data engineering solutions for RAN reliability and customer deployments. Build ingestion and processing pipelines, AI-ready datasets, and secure integrations into Git, Gerrit, CI/CD, testing, and development workflows. Transform prototypes into maintainable products while ensuring data quality, traceability, access control, monitoring, and vulnerability management. Collaborate with engineers, data scientists, architects, and product stakeholders, provide mentorship, and promote responsible AI-assisted development.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Build scalable AI and machine learning platform capabilities across model development, deployment, monitoring, cataloging, tagging, and LLM hosting. Collaborate with customers and cross-functional teams to integrate platforms such as AWS SageMaker and Azure ML. Develop secure APIs and distributed backend systems, optimize cloud and containerized services, and establish robust testing and CI/CD pipelines.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Designs and maintains reliable data pipelines and computed tables using SparkSQL and PySpark. Integrates IoT, product, customer, external, unstructured, and ML-related data into scalable data models supporting analytics, causal inference, model training, and dashboards. The role requires ETL development, data orchestration, API integration, strong data quality and uptime, and collaboration with Data Science, AI/ML, Analytics, and engineering teams. This is a remote position for candidates residing in Canada.

Build and scale reliable Snowflake data pipelines, ML pipelines, monitoring systems, and AI-driven agentic tooling that automates pipeline generation, testing, and maintenance. The role partners with analysts, data scientists, product teams, and analytics engineers to deliver scalable end-to-end data infrastructure for Babylist’s e-commerce platform.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

CommBank is seeking Indigenous job seekers for various roles across its sectors, offering traineeships, internships, and direct employment opportunities with support from an Indigenous Careers team.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Partner with sales teams to win strategic data security opportunities. Deliver technical presentations, gather customer requirements, lead product evaluations, configure solutions, and design test plans. The role requires extensive pre-sales experience, security and networking expertise, data protection knowledge, AI fluency, and collaboration across matrixed go-to-market and partner teams.

Maintain accurate manufacturing master data across ERP, PLM, MES, and analytics systems. Audit item records, identify discrepancies, investigate root causes, document data structures and governance rules, and support automated quality checks, dashboards, ERP testing, and UAT. Collaborate with Manufacturing, Engineering, Supply Chain, IS/ERP, and data-entry teams to resolve routing, work-order, and status issues while improving data processes and system reliability.

Build and manage Palantir Foundry data pipelines, ontology components, applications, and AIP-powered agentic AI workflows. Lead solutions from prototype through production, guide architecture and code reviews, coach engineers, resolve technical blockers, and communicate design decisions and delivery risks to clients. Integrate AI with enterprise data, APIs, and business systems while owning technical delivery.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Analyze full-funnel B2B marketing performance using dashboards, SQL, attribution modeling, and CRM data. Build Tableau or Looker Studio reporting, query Redshift data, and apply Python, AI APIs, prompt engineering, and automation to streamline data processing and performance summaries. Partner with campaign, web, and sales operations teams to improve tagging, KPI consistency, lead velocity, and conversion insights.