Top Data Jobs in Ottawa

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 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.

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.

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.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Reconstructs and redesigns data-driven presentations in Microsoft PowerPoint, creating visualizations and analytics reports that communicate complex insights clearly. Reviews and edits content for accuracy, flow, and narrative impact; applies data analysis and statistical reasoning; and refines slides based on feedback. The role supports AI training projects through high-quality presentation and analytical deliverables.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, experiments, and recommendations that drive decisions and measurable outcomes. Partner with cross-functional stakeholders, communicate insights clearly, and independently solve ambiguous problems using SQL and Python.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong analytical skills, independent data work, persuasive communication, and ownership of ambiguous problems with measurable business impact.

Own end-to-end data science and analytics for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, first-principles problem solving, independent data interpretation, persuasive communication, and ownership of ambiguous business problems.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independent data work, strong analytical thinking, clear communication, and end-to-end ownership of ambiguous, high-impact initiatives.

Designs and implements enterprise data architecture solutions, including logical and physical data models, source-to-target mappings, data flows, and database strategies. Reviews source systems, validates data using advanced SQL, governs models against enterprise standards, and collaborates with business, architecture, and development teams. The role requires extensive data modeling, RDBMS, SQL, analysis, and enterprise architecture experience, with Python, Azure Data Lakehouse, Parquet, Delta Lake, and CASE tool experience preferred.

Build infrastructure for reproducible robot training and evaluation datasets, including multimodal ingestion, temporal alignment, provenance, quality controls, storage, versioning, replay, and reliable delivery. Own schemas, lineage, observability, failure recovery, safe backfills, and dataset reproducibility across video, sensor, command, intervention, outcome, and machine-health data.

Own and scale application security across Redpanda’s C++ engine, Go control plane, Console, and Rust/Go SDKs. Responsibilities include threat modeling, secure design reviews, SAST/SCA/DAST, fuzzing, secure code review, PSIRT and vulnerability disclosure, software supply-chain security, security release gates, and security champions. The role partners closely with engineering to improve secure development practices, address vulnerabilities, and protect authentication, authorization, encryption, audit logging, and multi-tenant isolation.

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.

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.

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.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

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.

Build and manage Palantir Foundry data pipelines, ontology components, applications, and AIP-powered agentic AI workflows. Lead solutions from prototype through production, guide architecture and code reviews, coach engineers, resolve technical blockers, and communicate design decisions and delivery risks to clients. Integrate AI with enterprise data, APIs, and business systems while owning technical delivery.

Analyze full-funnel B2B marketing performance using dashboards, SQL, attribution modeling, and CRM data. Build Tableau or Looker Studio reporting, query Redshift data, and apply Python, AI APIs, prompt engineering, and automation to streamline data processing and performance summaries. Partner with campaign, web, and sales operations teams to improve tagging, KPI consistency, lead velocity, and conversion insights.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Design, build, and scale backend data services, APIs, microservices, distributed systems, and high-volume data pipelines. Optimize PostgreSQL and Oracle databases, improve reliability and performance across regions, diagnose complex production issues, and mentor engineers. Partner with DBA and SRE teams while providing hands-on technical leadership and owning services end to end.

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.

Leads the enterprise data engineering capability, including architecture, integration patterns, cloud data platforms, scalable pipelines, APIs, data governance, and real-time processing. Establishes standards and roadmaps across Azure, Snowflake, and MuleSoft while enabling business intelligence, automation, machine learning, RAG, vector search, and AI initiatives. Collaborates with stakeholders and technical teams to deliver governed, observable, high-quality enterprise data solutions.