Top Data Jobs in Ottawa

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.

Lead analytics and data science initiatives that optimize Coalition’s cyber insurance business. Responsibilities include experimentation, statistical analysis, funnel metric development, dashboarding, data modeling, scalable analytical workflows, executive reporting, roadmap recommendations, and mentoring junior analysts and scientists. The role partners with product, engineering, insurance, and revenue stakeholders to identify trends, improve customer experience, and support safe revenue growth.

Build and operate scalable analytics data platforms using Java, Python, Spark, Airflow, Iceberg, and AWS. Responsibilities include developing batch pipelines, automated testing frameworks, CI/CD validation, data quality controls, representative test datasets, and release gates. The role also advances AI-driven engineering workflows, supports production reliability through on-call participation and incident analysis, and collaborates with distributed engineering teams to improve platform quality, scalability, and observability.

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.

Owns analytical projects across customer onboarding, transaction behavior, and platform operations. Designs dashboards, KPI frameworks, reporting workflows, and data pipelines while delivering actionable insights. Partners with Engineering, Product, Payment Operations, Compliance, and Finance; mentors colleagues; manages project scope and priorities; resolves ambiguity; and drives cross-functional alignment. Requires advanced SQL and data modeling skills, BI tool experience, analytical scripting familiarity, FinTech domain knowledge, and 4+ years of analytics experience.

Leads OneStream-based forecasting, reporting, and demand-planning capabilities for Commercial FP&A. Builds reports and dashboards, manages data reconciliations, supports month-end reporting, optimizes platform performance, and partners with administrators and implementation teams. Drives AI and automation opportunities, delivers training, and translates financial requirements into scalable technical solutions.

Build and maintain OneStream forecasting, planning, reporting, and dashboard capabilities for Commercial FP&A. Develop demand planning models, reports, reconciliations, and performance monitoring processes while supporting month-end reporting and data improvement initiatives. Partner with finance, data, administration, and implementation teams to optimize the platform, automate processes, troubleshoot performance, deliver actionable insights, and develop internal OneStream expertise. The role also evaluates AI and automation opportunities across FP&A workflows.

Own end-to-end data science and analytics for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, analyses, reports, and experiments; surface insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings clearly, and driving measurable business outcomes.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires strong SQL or Python skills, independent analytical problem-solving, ownership of ambiguous initiatives, and persuasive communication with leadership.

Own end-to-end analytical work for a business unit across growth marketing, product, AI and automation, or strategic partnerships. Define business questions and metrics, build reports, conduct analyses, design experiments, and communicate insights and recommendations to stakeholders. The role requires independently structuring ambiguous problems, using SQL or Python, influencing leadership decisions, and driving measurable business outcomes.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; uncover insights; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data skills, persuasive communication, and ownership of ambiguous problems using SQL or Python.

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.

Own marketing analytics, funnel reporting, dashboards, attribution, cohort analysis, and conversion rate optimization. Analyze customer journeys from acquisition through retention, identify funnel losses, and design and evaluate A/B and multivariate experiments across landing pages, signup, onboarding, and email. Partner with marketing, product, engineering, RevOps, sales, and leadership to improve measurement, define trusted metrics, and translate findings into prioritized growth recommendations.

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.

Develop and maintain large-scale data and machine learning applications using Scala, Python, Spark, AWS, and modern DevOps practices. Responsibilities include designing data pipelines, deploying and monitoring machine learning models, optimizing distributed systems, supporting the full software lifecycle, documenting processes, and collaborating with data science, Data Ops, DevOps, and product teams in an Agile environment.

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.

Own data engineering for workforce forecasting and staffing products. Build and operate Databricks pipelines, lead the Redshift-to-Databricks migration, manage database replication and file ingestion, establish data contracts, handle PII and data residency, monitor quality and costs, and support on-call operations. The role also requires documentation, infrastructure-as-code, collaboration with product and analytics teams, and pipelines supporting forecasting or machine-learning models.

Leads analytics support for international sports fan growth and engagement. Partners with marketing, club, league, and regional stakeholders to define analytics opportunities, perform SQL and Python analysis, develop segmentation, propensity, churn, forecasting, and customer lifetime value models, and translate findings into business recommendations. Owns reporting and data quality, presents to senior audiences, mentors analysts, coordinates team priorities, and collaborates with data engineering and external partners.

Leads the enterprise data and integration strategy, architecture, governance, engineering standards, and technology roadmap. Builds and mentors a Data Engineering team, establishes scalable integration and data management practices, oversees platforms and vendors, and enables analytics, AI, automation, and digital transformation. The role partners with business, technology, cybersecurity, and architecture leaders to standardize data capabilities, improve quality and security, reduce technical debt, and deliver sustainable enterprise solutions.