Top Data Jobs in Ottawa

Owns the lifecycle of international data products from business requirements through engineering delivery, release, and production monitoring. The role investigates data issues using SQL and Python, validates data quality, defines acceptance criteria, manages requirements and traceability, and translates findings into actionable business insights. It partners closely with stakeholders and data engineering teams to resolve incidents, improve data trust, and establish monitoring and stewardship practices.

Build and optimize data pipelines, applications, ETL processes, data models, warehouses, integrations, and reporting solutions for financial services clients. The role analyzes requirements, performs data profiling and source-to-target mapping, supports cloud and data migration initiatives, documents technical approaches, collaborates with client and project teams, contributes to internal accelerators, and mentors junior consultants.

Leads the enterprise data platform engineering team and owns its reliability, security, scalability, performance, cost, and operational support. Oversees relational, NoSQL, and analytical platforms; data movement and transformation; infrastructure as code; GitOps; database automation; high availability and disaster recovery; monitoring; compliance; and incident response. Manages engineers, on-call operations, technology selection, vendor evaluations, budgeting, and cross-functional delivery while remaining hands-on in architecture, administration, performance tuning, and complex technical escalations.

Analyze Gecko engine telemetry across desktop and Android to identify data-quality issues, including bot contamination and hardware noise. Build automated dashboards, audit telemetry collection gaps, develop data-sanitization methods, and produce statistical analyses connecting technical performance metrics with user behavior and engagement. Translate complex engineering data into actionable product and budget recommendations for leadership while working independently across Performance, Data, and Product teams.

Owns partner marketing operations across Salesforce reporting, campaign data, MDF budgeting and reconciliation, partner portal administration, training content, and system integrations. The role analyzes partner engagement and campaign performance, maintains accurate records across platforms, troubleshoots portal issues, manages partner access, and coordinates with Marketing, Sales, Finance, IT, and external partners. Success requires strong organization, project management, data analysis, Salesforce, Excel, and PRM platform experience.

Build and validate transformed data, develop dbt models, manage Airflow scheduling, and support data pipelines. Analyze data and present recommendations to stakeholders, ensure insight accuracy, implement data exploration tools, and mentor team members. The role requires collaboration across business and engineering teams, strong SQL expertise, and experience with BigQuery, dbt, Airflow, Python, and relational databases.

Own end-to-end analytical support for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, independent data interpretation, first-principles problem solving, clear communication, and ownership of ambiguous business challenges.

Own end-to-end analytical functions for growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and influence stakeholders toward decisions that improve key business outcomes. The role requires independent data work, strong problem-solving, persuasive communication, and ownership of ambiguous problems.

Own end-to-end analytical support for a business unit in growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems using SQL and Python.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, establish metrics, build reports, analyze data, design experiments, communicate insights, and drive decisions with cross-functional stakeholders. The role requires strong analytical judgment, independent execution, first-principles thinking, and persuasive communication.

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, builds, automates, and optimizes enterprise cloud data pipelines and platforms. Responsibilities include ETL/ELT, DataOps, CI/CD, data migration, data modeling, data warehousing, lakehouse architecture, database administration, performance tuning, and production support. The role uses Databricks, Azure services, PySpark, SQL, and multiple cloud platforms to deliver scalable structured and unstructured data solutions.

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.

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.

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 marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, and segmentation. Partners with Marketing and Finance to establish performance goals and translate complex data into actionable recommendations. Automates reporting using analytics platforms, marketing automation tools, experimentation methods, and AI-enabled workflows while identifying opportunities to grow and retain high-value consumers.

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.

Leads advanced data analysis, develops dashboards, KPIs, and analytical models, and translates business problems into data-driven solutions. Works with complex datasets using Databricks, SQL, Python, BI tools, and cloud data platforms. Partners with engineering and governance teams, promotes analytics standards, mentors analysts, and communicates insights to stakeholders. Supports strategic decisions through descriptive, predictive, and prescriptive analytics.