Top Data Jobs in Ottawa

Coordinate customer onboarding, training, support, and retention workflows. Follow up on customer issues, align Sales, Accounting, Software, integrations, and licensing teams, and drive commitments through resolution. Build and improve operational processes, track projects, dependencies, blockers, and next steps, identify improvement opportunities from feedback, and provide clear updates across teams.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

Own marketplace product data quality and discoverability across product information, taxonomy, content, images, compliance, and marketplace feeds. Maintain marketplace requirements, map PIM data, run quality checks, resolve feed errors and suppressions, and track products through launch and sales. Diagnose poor visibility or conversion and coordinate root-cause fixes with business and content teams. Support SEO, marketplace search ranking, structured data, and AI-driven commerce initiatives.

Owns enterprise data architecture across on-premises and cloud environments. Designs data models, integration patterns, platforms, governance standards, security controls, and migration roadmaps. Evaluates data technologies, supports Azure modernization, ensures compliance with CUI, NIST SP 800-171, and CMMC 2.0, and documents architecture decisions. Partners with technical, security, infrastructure, application, and business teams while mentoring engineers and analysts and communicating recommendations to executives.

Own and improve Life360’s Ads Data Engineering platform on Databricks. Build, operate, and optimize batch and streaming pipelines, Delta Lake tables, dbt models, semantic layers, and core metrics. Integrate advertising and business data, implement quality and governance controls, manage AWS infrastructure with Terraform, reduce compute costs, and mentor engineers. Partner with analysts, ad strategists, and sales teams to deliver trusted data products while developing AI-assisted engineering tools and workflows.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, strong communication, and ownership of ambiguous initiatives that materially improve business outcomes.

Own end-to-end data science for a business area such as growth, product, AI, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Develop and maintain ETL processes, SQL Server databases, SSAS models, SSRS reports, and Power BI dashboards. Build unified data models across ERP systems, improve BI performance and data quality, and establish standardized development practices. Collaborate with finance, sales, supply chain, and other stakeholders to translate business requirements into scalable data and reporting solutions. Support modernization of the organization’s data environment and contribute to strategic business intelligence initiatives.

Own end-to-end data science for growth, product, 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, independent analytical problem-solving, clear communication, and ownership of ambiguous initiatives.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights and recommendations; and drive measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data work using SQL or Python, ownership of ambiguous problems, and persuasive communication with leadership.

Lead revenue growth for Execo’s AI data services business. Own annual bookings and revenue targets, define go-to-market strategy, lead complex enterprise deals, expand strategic accounts, develop technology partnerships, and build scalable sales motions. The role requires executive-level engagement with AI labs, model builders, and enterprise clients, along with disciplined forecasting, pipeline management, pricing input, and collaboration with delivery and marketing teams.

Leads a data engineering team while owning CapIntel’s data platform, including serverless services, batch and streaming pipelines, workflow orchestration, search indexes, observability, data quality, and incident response. The role combines people leadership, hands-on engineering, client data onboarding, architecture evolution, and cross-team enablement. Success includes improving reliability, data quality, delivery predictability, onboarding, and team ownership while safely applying AI tooling to financial data.

The Data Librarian researches, evaluates, organizes, maintains, and provides access to statistical and geospatial datasets. The role supports parliamentary clients through data analysis, visualization, geospatial analysis, mapping, quality control, and reference services. Responsibilities include assessing information needs, validating data accuracy and completeness, conducting in-depth searches, and responding to data and statistics requests.

Conduct adversarial research against anti-bot and web fingerprinting systems, reverse engineer defenses, build and improve HTTP client solutions and machine-learning data collection methods to scale Bright Data's web data collection platform.

Build and maintain scalable data pipelines, integrations, and data models connecting product, customer, sales, billing, and revenue data. Partner with Sales, RevOps, Finance, and Product to support forecasting, reporting, customer intelligence, segmentation, retention, and expansion analysis. Implement data validation, monitoring, documentation, and governance while improving infrastructure and automation for the company’s growing B2B operations.

Provides L3 technical support for enterprise distributed systems, troubleshooting configuration, hardware, product, and customer issues. Partners with engineering on resolution and root cause analysis, builds support tooling and services, participates in global on-call rotations, and collaborates with Customer Success to improve customer satisfaction. Requires expertise in Linux, Kubernetes, cloud platforms, containerization, Bash or Python, and distributed systems, with AI tooling experience preferred.

Own the Data Platform product strategy and roadmap for SandboxAQ’s AI Simulation division. Lead ingestion, schema reconciliation, knowledge graphs, lineage, provenance, governance, partner data sharing, and developer-facing APIs. Partner with engineering, science, design, compliance, CROs, and lab partners to create scalable, measurable products supporting drug discovery and materials research. Define metrics for dataset usability, matching accuracy, and lineage completeness while enabling secure, compliant data use by scientists and AI agents.

Performs ETL data migration from legacy utility billing systems into SpryPoint’s platform. Develops Python and SQL scripts, profiles source databases, maps and transforms datasets, validates data quality, and supports reliable go-live integrations. Collaborates with technical and client teams, documents best practices, improves automation and observability, and participates in iterative reviews. This early-career role emphasizes data conversion, auditing, process optimization, and continuous learning.

Owns QA/QC verification across the equipment lifecycle for mission-critical data center projects. Responsibilities include managing factory witness and acceptance testing, reviewing submittals and documentation, validating asset tags, inspecting installed equipment, documenting discrepancies, leading root cause analysis, coordinating commissioning and turnover activities, tracking corrective actions, and reporting material conditions to Operations. The role partners with Procurement, Engineering, Construction, Commissioning, general contractors, and site teams to ensure objective evidence, accountability, and complete issue closure.

Perform AI data collection, evaluation, annotation, pairwise comparisons, counting, and object tagging across audio, video, images, and other content. The role requires native-level Canadian French and fluent or advanced English. Prior experience with machine learning tasks, data preprocessing, quality assurance, or data annotation is preferred. This is flexible, part-time freelance work conducted fully remotely.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

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.

Own NerdWallet’s machine learning product roadmap from opportunity discovery through production and adoption. Partner with Data Science, ML Engineering, Data Engineering, Product Engineering, and business teams to define objectives, prioritize initiatives, manage technical dependencies, evaluate model performance, and measure business impact. Lead cross-functional delivery, influence senior stakeholders, establish ML product strategy, and mentor product managers and partners without formal authority.