Top Data Jobs in Ottawa

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.

Leads the enterprise data and integration capability, developing data strategy, governance frameworks, architecture standards, integration services, technology roadmaps, and data engineering practices. Oversees platforms, APIs, data pipelines, metadata, lineage, data quality, analytics, AI readiness, and modernization initiatives. Builds and mentors a Data Engineering team while partnering with business, technology, cybersecurity, infrastructure, architecture, and external stakeholders to establish secure, scalable, governed enterprise data solutions.

Lead data and business intelligence initiatives by translating business needs into scalable data solutions, standardized KPIs, reporting logic, and analytical use cases. Partner with stakeholders, engineers, architects, and BI teams across data integration, transformation, modeling, governance, and reporting. Validate data, investigate discrepancies, improve data quality, and provide technical leadership for cross-functional data initiatives.

Partner with sales teams to win strategic data security opportunities. Deliver technical presentations, gather customer requirements, lead product evaluations, configure solutions, and design test plans. The role requires extensive pre-sales experience, security and networking expertise, data protection knowledge, AI fluency, and collaboration across matrixed go-to-market and partner teams.

Leads the design, development, and operation of AOF’s data platform, including data lakes, warehouses, ingestion, orchestration, governance, reporting, and AI capabilities. Builds internal and customer-facing products using Python, Go, TypeScript, Terraform, and AWS. Drives architecture, security, CI/CD, cloud migration, regulatory reporting, and responsible AI deployment while partnering across business teams. Initially serves as a hands-on individual contributor and later grows an engineering team.

Leads end-to-end delivery of investment data solutions, translating business objectives into requirements, data mappings, lineage, testing, and implementation plans. Analyzes and reconciles complex datasets using SQL and Python, supports data governance and quality, coordinates UAT and defect resolution, and develops Power BI reports and dashboards. Partners with investment, operations, risk, compliance, technology, vendors, and senior stakeholders to deliver reliable analytics and reporting solutions.

The Investment Technology Business Analyst will deliver front-office technology and data implementations for asset and wealth management clients. Responsibilities include business analysis, SQL-based data work, data modeling, governance and quality management, cloud data platform implementation, and expertise across investment processes such as risk, performance, compliance, reporting, derivatives, operations, or investment data. The role also involves leading small to medium-sized consulting teams, developing technical solutions, managing client relationships, and traveling as needed.

Leads pre-sales discovery, solution architecture, strategic advisory, scoping, SOW development, RFP responses, and deal strategy for large-scale data, AI, and cloud transformations. Designs GCP-based data, ML, GenAI, and agentic solutions; facilitates executive workshops; shapes industry-specific offerings; supports opportunities exceeding $1 million; and serves as a trusted technical advisor to C-suite and architecture stakeholders.