Top Data Jobs in Ottawa

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Own and deliver highly available, low-latency backend identity systems supporting consumer onboarding, authentication, and verification. Lead quarterly team goals, guide engineers through complex problems, collaborate with product and design stakeholders, improve technical and code quality standards, monitor system availability, support on-call operations, and mentor team members.

Serve as the technical anchor for Domino’s identity and authorization platform. Design scalable, relationship-based authorization using SpiceDB; guide migration from legacy access checks; build cloud-native identity integrations; improve platform reliability, security, and architecture; and advise engineering teams on authorization, APIs, abstractions, and AI-assisted development practices.

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.

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.

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 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 demonstrates secure, scalable Azure database and analytics solutions for enterprise customers. Leads technical sales engagements, proofs of concept, hackathons, architecture workshops, migrations, and production deployments. Advises platform leaders, resolves technical blockers, collaborates with engineering, and develops expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, on-premises data warehouses, Hadoop, and competing analytics platforms.

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 production-grade web scraping, content integration, and data pipelines using TypeScript, Java, Kafka, and Solr. Develop event-driven services, REST APIs, parsing and normalization logic, and integrations with content providers and external feeds. Improve pipeline reliability, observability, performance, and data quality while contributing to architecture and technical direction. The role also includes mentoring junior developers and supporting high-volume media content processing.

Develop backend services, event-driven Kafka pipelines, web scrapers, and content integrations that collect, process, and index online media. Build Java REST APIs and TypeScript/Nest.js scraping systems, integrate third-party feeds and APIs, improve content quality, and collaborate with product and engineering teams. Contribute to testing, code reviews, refactoring, and continuous improvement across distributed data-processing systems.

Leads and develops a software engineering team building web crawling, content collection, mention-matching, integrations, and large-scale data pipelines. Sets architecture and technical direction, ensures scalable and reliable systems, manages incident response, monitors data quality and coverage metrics, and partners with Product, Customer Success, vendors, and Legal. Contributes to AI workflows while supporting compliance with platform, privacy, copyright, and licensing requirements.

Maintains project and amendment setup in the accounting system, processes change orders and fee increases, supports contract administration and audits, prepares project reports, identifies discrepancies and risks, and collaborates with project managers and Finance and Accounting teams.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Develops data-driven solutions for credit cards and personal lending by extracting and preparing large datasets, applying statistics and advanced analytics, visualizing insights, and communicating recommendations to business stakeholders. Partners with data scientists, analysts, engineers, and developers to create scalable machine learning and data science solutions. Provides thought leadership, explains statistical methods, and supports knowledge sharing across the organization.

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.