Top Data Analyst Jobs in Ottawa

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.

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.

Analyze user acquisition, advertising revenue, game portfolio, and finance data. Build dashboards and automated reports, identify trends, support campaign performance analysis, improve data workflows, and maintain reporting accuracy. The role requires SQL, Python or R, quantitative modeling, cohort analysis, LTV forecasting, and strong communication with cross-functional stakeholders. Familiarity with experimentation, AI-assisted coding, modern databases, big data, and performance marketing is preferred.

No responsibilities or qualifications were provided. The available job title indicates a Business Analyst role, generally focused on supporting business processes and analysis.

Build Databricks data pipelines and reliable data models, then design Power BI dashboards and reports using DAX, row-level security, and efficient modeling. Collaborate with clients, developers, product teams, and IT stakeholders to gather requirements, deploy solutions, improve data quality, and optimize dashboard performance. The role supports nuclear utility and reactor project clients in a hybrid environment with regular client-site travel.

Maintain accurate manufacturing master data across ERP, PLM, MES, and analytics systems. Audit item records, identify discrepancies, investigate root causes, document data structures and governance rules, and support automated quality checks, dashboards, ERP testing, and UAT. Collaborate with Manufacturing, Engineering, Supply Chain, IS/ERP, and data-entry teams to resolve routing, work-order, and status issues while improving data processes and system reliability.

The Data Analyst develops and maintains Power BI dashboards, datasets, data models, SQL and DAX solutions, and data pipelines. The role supports AWS cloud analytics integration, data quality, troubleshooting, governance, documentation, and workspace administration. It coordinates data engineers, translates business requirements into technical deliverables, collaborates with stakeholders, and communicates findings to nontechnical audiences while supporting agile delivery and data privacy compliance.

Analyze large datasets to produce dashboards, metrics, and insights supporting product, care, and business decisions. Build BI infrastructure, run A/B and multivariate tests, perform ad-hoc analyses and visualizations, and collaborate with stakeholders and data scientists to inform strategy and operations.

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.

Analyzes financial and operational data to support decision-making across Customer Operations and Finance. Responsibilities include maintaining IFRS 9 expected credit loss models, writing SQL queries, developing financial and trend analyses, managing data warehouses, creating Power BI dashboards, improving management reporting, supporting audits, and automating processes with scripting or programming. The role collaborates with finance, analytics, and cross-functional stakeholders to deliver accurate insights and reporting.

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.

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.

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.

Analyze the customer lifecycle across activation, engagement, retention, expansion, and renewal for a product-led SaaS business. Partner with Customer Success, Product, Growth, RevOps, and Data Engineering to develop analytical frameworks, identify growth opportunities, evaluate experiments, and provide actionable recommendations to senior leaders. Build data foundations and standards supporting digital success, AI, automation, forecasting, anomaly detection, and generative BI.

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.

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

Own end-to-end financial data analysis, including SQL and dbt data modeling, Looker dashboard development, data quality validation, reconciliation, and stakeholder-facing insights. Partner with Finance and Payments to support reporting, planning, revenue analysis, cost insights, and transaction-level analytics. Translate ambiguous business needs into reliable technical solutions while improving documentation, QA, modeling, and data architecture practices.

Build and maintain BI dashboards, reporting tools, and automated data workflows. Analyze customer behavior, funnels, retention, engagement, and product adoption to deliver actionable insights across Product, Operations, Marketing, Finance, and Leadership. Use AI tools and APIs to improve analytics workflows, self-service exploration, data-quality checks, and insight generation while validating accuracy, privacy, and business impact.

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.

Analyze user behavior and product performance, create reports and dashboards, and provide data-driven insights to teams for decision-making.

As a Data Analyst at Stripe, you'll build data products, metrics, and dashboards, analyze large datasets, and communicate insights to drive business decisions.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

As a Data Analyst, you will analyze user behavior and product performance, creating reports and dashboards to aid decision-making and optimize user experience.

Analyze go-to-market performance across marketing, sales, revenue operations, and finance. Build reporting connecting campaigns to pipeline and revenue, investigate funnel and attribution questions, support budget analysis and planning, improve data quality, and communicate insights to technical and non-technical stakeholders. Collaborate with AI and Data Engineering teams on marketing data agents, architecture, and source tables.

Partner with Marketing as the embedded data science lead to build marketing measurement, attribution, LTV/ROAS/CAC models, run experiments, analyze the full funnel, create forecasts and dashboards, and influence channel strategy and budget allocation.