Top Data Jobs in Ottawa

Design, build, and scale backend data services, APIs, microservices, distributed systems, and high-volume data pipelines. Optimize PostgreSQL and Oracle databases, improve reliability and performance across regions, diagnose complex production issues, and mentor engineers. Partner with DBA and SRE teams while providing hands-on technical leadership and owning services end to end.

Leads the enterprise data engineering capability, including architecture, integration patterns, cloud data platforms, scalable pipelines, APIs, data governance, and real-time processing. Establishes standards and roadmaps across Azure, Snowflake, and MuleSoft while enabling business intelligence, automation, machine learning, RAG, vector search, and AI initiatives. Collaborates with stakeholders and technical teams to deliver governed, observable, high-quality enterprise data solutions.

Leads enterprise data engineering and AI platform strategy, including Azure, Snowflake, and MuleSoft architectures; scalable pipelines, APIs, data lakes, warehouses, governance, integration, real-time processing, and AI capabilities such as RAG and vector search. Partners with stakeholders to deliver trusted data products supporting analytics, automation, reporting, machine learning, and enterprise AI initiatives.

Supports client engagements involving data de-identification and re-identification risk measurement, particularly for healthcare data. Responsibilities include validating and preparing datasets, conducting qualitative and quantitative risk analyses, documenting findings in client reports, preparing presentations, supporting solution delivery, coaching clients, and developing expertise in statistical disclosure control, privacy regulations, and de-identification standards.

Owns the technical and statistical integrity of payment data analytics workflows. Responsibilities include SQL and Python data ingestion, transformation, validation, quality controls, feature engineering, time-series analysis, model readiness, back-testing support, GCP analytics environment management, workflow automation, documentation, and translating findings for product and executive stakeholders.

Collects, extracts, analyzes, interprets, and validates data to support business decisions. Develops and maintains management indicators, reports, data analytics solutions, quality-control procedures, documentation, and training materials. Participates in SAS ETL development, supports data governance and publication quality, and assists users with data requests and interpretation.

Designs and delivers secure, scalable Azure cloud, data, analytics, database, and AI solutions for customers. Leads architecture, roadmaps, proofs of concept, production deployments, workload optimization, governance, resiliency, and security initiatives. Partners with technical and business stakeholders, supports customer adoption and Azure consumption, resolves technical blockers, contributes reusable deployment frameworks, and mentors others. Requires strong expertise in Microsoft Fabric, Azure Databricks, Purview, Azure databases, data engineering, governance, migration, and cloud architecture.

Develop and optimize secure networking software for Ribbon’s next-generation products across cloud, server, and custom hardware platforms. Responsibilities include data-plane acceleration using network processors, debugging customer issues, collaborating with system testing, and measuring product performance. The role requires strong C and embedded programming experience, networking expertise, Linux and kernel knowledge, and familiarity with technologies such as DPDK, VPP, SmartNICs, Kubernetes networking interfaces, tunneling, and eBPF.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Leads HR data, people analytics, workforce insights, Power BI dashboards, automation, AI-enabled solutions, data governance, HR technology roadmaps, and digital transformation. Partners with HR, IT, business leaders, and vendors to improve decision-making, optimize processes, support adoption, and develop future-state HR operating models.

Leads HR data analytics and digital transformation by developing Power BI dashboards, workforce insights, automation, AI-enabled solutions, data governance, and HR technology roadmaps. Partners with HR, IT, business leaders, and vendors to improve processes, guide decisions, support adoption, and deliver sustainable change across HR operations.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Sets the technical vision for data science, leads complex credit risk, pricing, and loss forecasting models from conception through production, and architects scalable machine learning pipelines. Partners with senior stakeholders to shape strategy, establishes modeling and validation standards, mentors technical staff, and communicates recommendations to executives. The role requires deep fintech lending experience, advanced statistical and machine learning expertise, and hands-on production model deployment.

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.

Develop customer-specific AI solutions for consequential business decisions. Investigate internal and public information, evaluate evidence, identify uncertainty, and design reliable AI workflows. Work directly with customers to understand needs, prototype solutions, test them with users, and improve their usefulness. Translate successful workflows into repeatable product capabilities while partnering with engineers on integrations and missing technical functionality.

Owns Partner Marketing operations across Salesforce reporting, campaign data, MDF budgeting and reconciliation, partner portal administration, training content, and system integrations. The role maintains data accuracy, builds dashboards, tracks partner engagement and campaign performance, troubleshoots portal issues, manages partner access, and coordinates with Marketing, Sales, Finance, IT, and partners. It requires strong Salesforce, Excel, project management, data analysis, and partner portal experience.

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.

Lead enterprise client reporting and manage Proof’s data team. Own report accuracy, delivery, client relationships, data pipelines, shared models, BI tooling, and internal data insights. Coach team members, guide technical decisions, review SQL and data models, partner with Engineering and Product, resolve reconciliation issues, and improve automation. The role will establish data quality standards and help the company use trusted data to support growth, internal tools, and AI systems.

Leads company-wide measurement and causal strategy, defining canonical metrics, semantic data architecture, experimentation standards, and strategic models such as marketing mix, elasticity, incentive sensitivity, and forecasting. Partners with engineering and analytics teams to build scalable, production-ready analytical frameworks, improve scientific rigor, and guide executive decisions. Provides organization-wide technical leadership, develops modeling standards, and applies advanced statistical and causal methods to high-impact business problems.

Own the scientific and product roadmap for Fetch’s monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead methodology, evaluate evidence, balance reach, precision, revenue, and cost, and convert bespoke studies into scalable products. Partner with Product, Engineering, Sales, and GTM on pilots, positioning, and customer value. Remain hands-on with SQL, Python, statistical modeling, and causal inference while mentoring scientists and influencing senior stakeholders.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Own the data science strategy, analytical roadmap, and experimentation for ranking and relevance. Partner with engineering, product, commercial, and offer teams to improve advertiser outcomes, user value, retention, and marketplace economics. Design rigorous experiments and observational analyses, quantify incremental business impact, establish KPI frameworks and trade-off guardrails, influence launch and investment decisions, mentor colleagues, and build reusable analytical methods using large-scale data systems.

Owns the roadmap and product strategy for private markets, shareholder, and deals financial data. Responsibilities include identifying content gaps, defining requirements and PRDs, improving data quality and discoverability, collaborating with platform, engineering, design, and go-to-market teams, conducting QA, and driving adoption among financial analysts, investment bankers, and corporate development professionals.

Own the product roadmap for surfacing financial data through search, screening, and agentic capabilities. Define requirements, PRDs, and prioritization; collaborate with engineering, design, data platform, AI/ML, and go-to-market teams; and lead feature development from discovery through launch. Improve content structure, tagging, indexing, ranking, search relevance, screening precision, and agentic retrieval reliability for financial analysts, bankers, and corporate development users.

Supports data extraction, management, quality assurance, cleaning, validation, harmonization, statistical and qualitative analysis, and public health program evaluation. Develops analytic datasets and contributes to technical memoranda, reports, presentations, tables, and figures. Analyzes implementation measures and mixed-methods data, interprets findings, and recommends program improvements. Requires collaboration with government and public health stakeholders, use of Microsoft Office and CDC administrative systems, and the ability to obtain and maintain required customer clearance.