Top Data Jobs in Ottawa

Support client engagements measuring de-identification and re-identification risk for datasets (especially healthcare). Validate and prepare data, perform qualitative and quantitative risk analyses, document results in client-facing reports, prepare presentations/materials, assist deliveries and remote coaching, and develop expertise in privacy regulations and statistical disclosure control.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Reviews and evaluates clinical trial data within oncology studies, supporting data quality, consistency, and drug development objectives. Collaborates with clinical solutions and study teams to identify issues, apply therapeutic expertise, and help deliver reliable outcomes for life sciences customers.

Own GTM data accuracy, hygiene, governance, and integrity across Salesforce and related systems. Build data models, pipelines, Power BI or Tableau dashboards, and automated reporting for Sales and executive stakeholders. Analyze pipeline, forecasting, conversion, win/loss, productivity, and eventually Customer Success metrics. Document KPI definitions, train users, resolve workflow bottlenecks, and recommend improvements to data architecture and processes.

Leads the design, development, optimization, and support of enterprise-scale ETL/ELT pipelines and cloud data platforms. Builds Databricks and Spark processing solutions, dimensional data models, SQL transformations, and data quality controls. Troubleshoots production issues, improves performance and reliability, contributes to data architecture, and establishes engineering standards. Collaborates with technical and business stakeholders while mentoring engineers and supporting CI/CD, testing, monitoring, documentation, and modernization of legacy data warehouses.

Leads multidisciplinary data center design projects from proposal through closeout, coordinating engineering teams, schedules, budgets, deliverables, client relationships, design reviews, risk mitigation, quality compliance, contract administration, and change orders. The role supports business development, mentors junior staff, and requires occasional travel across Canada and the United States.

Lead enterprise and domain data modeling across conceptual, canonical, logical, physical, and business models. Translate business concepts into scalable data structures, facilitate stakeholder workshops, establish business glossaries, and align models with governance, metadata, MDM, analytics, AI, reporting, and data stewardship initiatives. Support engineering teams implementing cloud and lakehouse solutions while serving as a domain architecture lead for durable, flexible modern data platforms.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Design, build, optimize, and maintain scalable Databricks data pipelines and ETL/ELT workflows. Manage CI/CD with Azure Pipelines and Azure Key Vault, improve performance and cost efficiency, implement data quality and governance practices, troubleshoot distributed systems, collaborate with stakeholders, and support cloud data platform modernization initiatives.

Designs statistical analysis plans and validates machine learning models using rigorous performance, calibration, uncertainty, subgroup, fairness, and longitudinal analyses. Applies biostatistical methods to healthcare data, documents assumptions and limitations for auditability and regulatory readiness, and translates complex findings into clinically meaningful insights. Collaborates with engineering, machine learning, data, clinical, and product teams.

Lead the Data Modeling team, focusing on the development of reusable core data models and metrics. Collaborate with various teams to ensure data quality and reliability in analytics and AI/ML. Hands-on role requiring experience in data modeling, SQL, Python, and Spark.

Build and own marketing measurement, attribution, experimentation, and analytics for performance, influencer, and lifecycle marketing. Responsibilities include creating dashboards, debugging attribution across advertising platforms and CRM systems, designing incrementality and A/B tests, analyzing CAC, ROAS, payback, cohorts, LTV, and channel efficiency, developing attribution taxonomies and instrumentation, and translating findings into recommendations for marketing, product, finance, and executive stakeholders.

Lead Mozilla’s Marketing Data Science strategy and roadmap for Firefox. Drive attribution, ROI, campaign measurement, budget optimization, causal lift testing, brand impact analysis, retention and LTV modeling, audience segmentation, reporting infrastructure, dashboards, and scalable data tools. Mentor and develop team members, partner with cross-functional data science and marketing leaders, translate complex analyses into actionable recommendations, and evaluate emerging AI tools to improve workflows and business impact.

Lead the architecture and implementation of complex cloud-based data solutions for clients. Define strategic data roadmaps, support pre-sales engagements as a data engineering subject matter expert, conduct technical interviews, mentor architects and engineers, and establish data standards and operating procedures. The role requires extensive AWS and complex data systems experience, expertise across traditional and modern data technologies, strong documentation practices, and excellent communication.

Provide on-site configuration and data management support for NASA AFRC projects: maintain CM plans and repositories, control configuration items and engineering changes, administer CCBs and ACMS/ACCT, run audits (PCAs), track action items and metrics, and train users on CM tools and processes.

Annotate and segment videos, write objective captions, label visually verifiable actions, and review AI-generated captions for accuracy and consistency. Specialists must follow detailed guidelines, maintain at least 95% benchmark accuracy, and complete a paid certification process involving training, platform practice, and five certification tasks before accessing ongoing production work.

CommBank is seeking Indigenous job seekers for various roles across its sectors, offering traineeships, internships, and direct employment opportunities with support from an Indigenous Careers team.

Lead the migration of Vanta’s compliance data platform from MongoDB to schema-aware PostgreSQL while maintaining backward compatibility and data integrity. Design high-throughput Kafka and S3 pipelines, eventual-consistency safeguards, query APIs, tenant isolation, caching, and distributed storage systems. Set architectural direction, mentor engineers, drive cross-team technical roadmaps, and improve reliability, observability, and operational practices for auditable enterprise-scale infrastructure.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead a hybrid engineering and data platform team to build scalable software and data infrastructure. Oversee Power Platform deployments, data architecture, governance and compliance (GDPR/HIPAA), BI/analytics, DevOps automation, and integration of AI/LLM and agentic solutions to drive intelligent automation and platform scalability.

Analyze product usage, feature adoption, telemetry, and financial data to generate actionable insights. Own data modeling, dashboards, KPI development, and end-to-end analytics delivery while partnering with stakeholders on product initiatives. Mentor associate-level analysts and maintain strong execution metrics for analytics work. The role requires advanced SQL and Python, Databricks and PySpark expertise, visualization experience, and SaaS product analytics experience.

Lead the architecture and development of Blackpoint Cyber’s enterprise data platform. Own data ingestion, storage, transformation, governance, observability, lifecycle management, and cost optimization. Design scalable pipelines and medallion architecture for cybersecurity events, survey data, business intelligence, analytics, reporting, and AI. Drive multi-cloud integrations, data quality, reliability, compliance, and platform roadmap decisions while mentoring senior data engineers and collaborating across Product, Engineering, AI, and Security teams.

Perform accurate remote data entry: enter, update, verify, and correct records; organize digital files; process forms and invoices; ensure confidentiality and compliance; meet productivity and accuracy targets; collaborate with team to improve workflows.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.