Top Data Jobs in Ottawa

Leads the enterprise data and integration function by developing strategy, architecture, governance, standards, roadmaps, and scalable data capabilities. The role oversees integration architecture, APIs, data platforms, pipelines, metadata, data quality, analytics, AI readiness, and modernization. It also leads a Data Engineering team, evaluates technologies and vendors, establishes governance, reduces technical debt, and partners with business, cybersecurity, infrastructure, applications, and architecture leaders.

Designs and deploys data integration, data management, reporting functionality, and data models for insurance-sector transformation projects. The role analyzes client needs, configures SAP solutions, applies data modeling expertise, and collaborates with software development and sales teams. Candidates need experience with IT transformation, insurance data processes, SAP FDS or SAP BW solutions, SAP HANA technologies, and technical mapping. The position is Canada-based with 50% travel and occasional on-site work in Calgary or Toronto.

Lead marketing analytics and data strategy for B2B SaaS Marketing. Design measurement frameworks, own attribution, funnel and lifecycle analysis, forecasting, and marketing investment recommendations. Partner with senior marketing leaders and cross-functional teams to build data foundations, enable AI-assisted analytics, and mentor analysts to raise analytical standards.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

Lead enterprise data architecture and transformation initiatives for financial-services clients. Responsibilities include defining target-state architectures, operating models, roadmaps, integration patterns, data governance, platform strategies, and architectural standards. The role evaluates vendors and build-versus-buy decisions, leads workshops and delivery teams, communicates with executives, manages complex projects, develops internal intellectual property, and mentors consultants. Strong expertise in MDM, data modeling, integration, cloud data platforms, governance, quality, security, and asset and wealth management data is required.

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.

Leads and scales a team of Technical Architects serving enterprise customers. Oversees customer onboarding, success, retention, expansion, technical escalations, implementation standards, and customer health metrics. Partners with Sales, Product, Marketing, and Engineering to improve adoption, prioritize customer-driven enhancements, and develop go-to-market strategies. Combines people leadership, technical expertise, coaching, and cross-functional program execution to deliver strong customer outcomes.

Own the vision and roadmap for Wrapbook’s Agreement Data Platform, including agreement data models, rules, APIs, operator tooling, validation, and governance. Partner with engineering and payroll teams on migrations, service boundaries, explainability, compliance, and reliable downstream calculations. Drive discovery through launch, define measurable success metrics, use data to prioritize decisions, and leverage AI tools daily for analysis, coding comprehension, writing, and prototyping.

Coordinate cross-functional programs across HR, Finance, and corporate functions from planning through execution. Manage employee lifecycle workflows, recruiting operations, compliance tracking, finance operations, audits, budgets, reporting, and leadership updates. Identify and implement AI-enabled workflows, translate processes into agent instructions, monitor accuracy and privacy, establish performance metrics, and train internal users. Partner with executives and functional leaders to improve operational processes and adoption of automation.

Lead product design for Waabi’s driverless commercial and operations ecosystem, including fleet management, remote assistance, dispatch, monitoring, and safety-critical workflows. Observe fleet operators and terminal staff, design human-AI interactions, extend the design system, and ship directly in React codebases using AI-assisted tools. Collaborate with product, engineering, research, and operations teams while helping establish design culture and processes as the second product designer.

Design and lead scalable data engineering solutions using Databricks and Azure. Establish enterprise data modeling and engineering standards, architect pipelines and integrations, improve platform reliability and performance, guide modernization and cloud migrations, troubleshoot complex issues, and mentor data engineers while ensuring security, quality, and governance.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

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.

Build highly available, scalable distributed systems and real-time data services supporting Internet-wide scanning, DNS resolution, certificate ingestion, security research, and customer applications. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, frameworks, and cloud or on-premises services; productionizing prototypes; and collaborating with engineering, product, machine learning, and security teams. The role emphasizes maintainable, testable code, reliability, monitoring, and long-term platform development.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Analyze complex structured and unstructured datasets, develop predictive models, apply statistical and machine learning methods, identify data quality issues, and generate actionable insights. The role integrates data from multiple sources, supports modernization and governance initiatives, creates dashboards and analytical frameworks, and communicates findings to technical and non-technical stakeholders. It is hybrid in Toronto with onsite work required five days as project needs dictate.

Validates, normalizes, merges, and quality-checks geological datasets including drillhole, geochemical, geophysical, and spatial data. Improves data-processing workflows, supports automation, beta-tests AI/ML tools, and provides geoscience expertise to product and engineering teams. Tracks client projects, professional-services hours, timelines, blockers, and escalations while transforming raw geological information into reliable inputs for software products and AI models.

Build and improve scalable full-stack products for higher education, owning initiatives across the software development lifecycle from discovery through release. Collaborate with Product, Design, and Engineering in a cross-functional Scrum team, experiment with AI technologies, resolve bugs, complete tickets, and deliver reliable, tested, user-focused features.

Manage interfaces and eligibility files for group insurance: perform daily data loading, validation and updates, document procedures, communicate with clients and stakeholders, and recommend process improvements and automation.

This role involves deploying solutions for clients, managing data compliance, and providing feedback for product improvement. Requires collaboration with teams and clients.

Build scalable GCP-based data pipelines and datasets for insurance fraud analytics. Develop and evaluate machine learning, NLP, and Generative AI models using claims and unstructured SIU data. Apply explainable AI techniques, conduct exploratory analysis, automate data workflows, document architectures, and support privacy, fairness, and compliance requirements while collaborating with investigators and business analysts.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Lead enterprise and domain data modeling across conceptual, canonical, logical, physical, and business models. Translate business concepts into scalable data structures, facilitate stakeholder workshops, establish business glossaries, and align models with governance, metadata, MDM, analytics, AI, reporting, and data stewardship initiatives. Support engineering teams implementing cloud and lakehouse solutions while serving as a domain architecture lead for durable, flexible modern data platforms.