Top Data Jobs in Ottawa

Build and maintain business-facing analytical applications, Tableau dashboards, reports, and Cube semantic models. Develop production code in Python and JavaScript, query Amazon Redshift, manage Salesforce reporting, and implement data quality validation, governance, access controls, and row-level security. Collaborate with cross-functional stakeholders to translate business needs into reusable data products, support data-driven decisions, and deliver projects through Git-based workflows, code reviews, and CI/CD.

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.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design and lead enterprise data platforms, including scalable lakes, warehouses, batch and real-time pipelines, governance, quality, monitoring, and modernization. Build data infrastructure for production AI/ML systems, including embeddings, vector stores, RAG preparation, feature stores, and training and inference flows. Partner with leadership and cross-functional teams on data strategy, establish engineering standards, make architectural decisions, and mentor engineers.

Leads product marketing strategy, positioning, messaging, go-to-market execution, AI adoption, product launches, market expansion, and customer-driven roadmap input. Builds and manages a high-performing product marketing team, partners with executive, product, revenue, and marketing stakeholders, and drives product adoption, retention, category growth, and revenue across programmatic advertising and martech markets.

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.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Leads planning and roadmap execution for data orchestration capabilities across ingestion, transformation, routing, synchronization, monitoring, and delivery. Defines requirements for reusable pipelines, data contracts, schemas, lineage, observability, and AI-ready data flows. Partners with engineering, architecture, data, security, customer teams, and stakeholders to prioritize capabilities, improve reliability and adoption, reduce custom implementations, and establish measurable outcomes for data trust and onboarding efficiency.

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 and develop five experienced analysts and data scientists while personally owning analytics across ecommerce, health, and supply chain. Partner with executives, establish KPI and reporting standards, prioritize the analytics roadmap, deliver strategic insights, and advance AI-native self-service analytics tools. The role combines people leadership, hands-on analysis, data governance, and business strategy in a remote-first environment.

Provide Workday HCM data conversion consulting across client implementations and post-production projects. Build, validate, and deploy data conversion, business process, and reporting solutions; guide requirements, configuration, testing, and production deployment; collaborate with engagement and sales teams; maintain project forecasts; advise on Workday best practices; and support client relationships.

Own the strategy, roadmap, architecture, and adoption of the company’s central data infrastructure and BI platform. Partner with analytics, engineering, and business teams to establish trusted KPI reporting, semantic models, scalable warehousing, BI and ETL tooling, clear SLAs, and AI-powered self-service insights. Translate ambiguous needs into testable requirements, evaluate technical solutions, prototype quickly, and measure adoption, quality, and reporting efficiency.

Build and operate Ground News’s data platform, including warehouse integrations, scalable dimensional models, analytical layers, and KPI infrastructure. Own ingestion pipelines, data quality, monitoring, incident response, infrastructure as code, and deployment workflows across AWS and GCP. Partner with analysts, ML engineers, and product stakeholders to create reliable, reusable data models and ensure accurate, observable, cost-effective data systems.

Manage the operational delivery of human data for a strategic AI customer. Responsibilities include translating requirements into workflows, managing participant pools and onboarding, tracking supply and quality, leading cross-functional projects, resolving operational issues, documenting processes, and improving scalability with Product and Engineering. The role also supports participant assessment and certification programs while serving as an operational point of contact for delivery teams.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Leads clinical data management activities from study start-up through database lock for complex clinical research projects. Oversees data quality, documentation, project teams, and multiple studies; ensures compliance with SOPs, sponsor requirements, ICH GCP, and regulatory guidelines. Provides technical leadership, mentoring, training, consulting, resource coordination, and expertise in clinical trial databases and EDC systems.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

Owns data stewardship for corporate transactions and regulatory matters, ensuring accurate, consistent, secure data across firm systems. Responsibilities include designing metadata models, resolving data quality issues, auditing dashboards, supporting migrations, verifying transactional information with attorneys, organizing data for due diligence, and assisting with governance policies. The role collaborates with knowledge management, information governance, records management, attorneys, and business services teams.

Leads presales activities for insurance software and geospatial analytics solutions, including customer discovery, requirements analysis, personalized demonstrations, presentations, proposals, RFP/RFI responses, and competitive materials. Partners with sales, product marketing, cloud operations, and professional services teams. Advises customers on cloud migration and integration requirements, supports implementation transitions, and presents at industry events. Requires substantial insurance-sector expertise, strong communication skills, AI enablement experience, and travel up to 50%.

Owns advanced reporting, dashboards, data analysis, KPI development, ETL requirements, and data quality initiatives for internal stakeholders. The role delivers recurring and ad hoc reports, builds automated workflows, documents data definitions and processes, communicates insights to senior leaders, and supports BI sprint planning. Strong SQL, data visualization, stakeholder communication, and systematic data quality problem-solving are required.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Lead event-driven and batch processing decisions, establish standards for observability, freshness, and failure handling, and guide cross-functional platform design. Mentor engineers, conduct design reviews, influence teams without direct authority, and drive foundational platform improvements. The role requires deep Python and SQL expertise, cloud data warehouse experience, scalable API-based ETL design, orchestration, and containerized infrastructure experience.

Build and maintain reliable data pipelines, integrations, APIs, and event-driven or batch processing systems. Transform data from advertising, commerce, and order management platforms while improving monitoring, data freshness, performance, security, and scalability. Partner with product, infrastructure, and data engineering teams to expand the integration platform and onboard new sources in a cloud-native, multi-tenant environment.

Manages data entry, repositories, quality checks, documentation, governance, reporting, and controlled data distribution across multiple projects. The role supports data tools, extracts, user onboarding, troubleshooting, and process improvement while ensuring accuracy, security, and compliance. It requires strong organization, analytical ability, attention to detail, and collaboration across teams. Experience with data management and Windchill PLM is valuable, particularly in engineering or defense environments.