Top Data Jobs in Ottawa

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, forecasting, targeting, and personalization. Collaborate with engineers and product teams, set modeling and productionization standards, mentor data scientists, guide architecture and experimentation, and apply cloud and MLOps best practices to drive measurable product and business impact.

Lead end-to-end data science initiatives: design, deploy, and monitor ML/statistical models for credit risk, pricing, collections and fraud. Collaborate cross-functionally to integrate models into production, define model development standards, mentor team members, and maintain scalable ML pipelines and monitoring systems to drive measurable business impact.

Leads the vision, architecture, strategy, and execution of enterprise data platforms supporting scalable analytics and AI initiatives. Oversees modern data technologies, production reliability, FinOps, governance, architecture standards, and cross-functional delivery. Builds and mentors engineering teams, communicates with executives, evaluates third-party technologies, resolves critical production issues, and drives continuous improvement. The role is remote with occasional planned travel to the Southfield, Michigan office.

Leads data engineering consulting engagements, serving as a subject matter expert and data architect. Designs scalable data environments, models, pipelines, integrations, warehouses, and visualizations while ensuring quality and governance. Leads architectural decisions, project delivery, technology strategy, and AI-augmented development practices. Mentors technical teams, collaborates with data scientists, analysts, clients, and stakeholders, and drives process improvements and innovation. The remote role may require infrequent travel to the United States.

Build and operate reliable data pipelines transforming flight test, simulation, and operational data into versioned, traceable ML training datasets. Responsibilities include ingestion, normalization, time alignment, validation, dataset lineage, anomaly mining, labeling workflows, deployment feedback loops, monitoring, and data coverage analysis. Collaborate with flight test and operations teams on instrumentation and data access. The role is on-site in Boston and requires production data engineering experience supporting machine learning pipelines.

Leads sales strategy, market expansion, enterprise deal execution, and team management for private markets software and data solutions across the Americas. Builds and manages sales, business development, and presales teams; owns pipeline performance, pricing, forecasting, and major client relationships. Personally closes complex enterprise agreements with financial institutions, collaborates with product, professional services, and customer success teams, and represents the company at industry events.

Design, build, and operate scalable data ingestion, replication, and lakehouse infrastructure to move petabytes of data into a Delta Lake on S3. Improve reliability, observability, security, and developer experience for Spark/Databricks processing. Develop internal libraries and tooling (Go/Python), collaborate with cross-functional teams, and help shape long-term data platform and AI-ready infrastructure.

Own production-grade analytics pipelines from design through maintenance, build dbt-based analytical models using Kimball dimensional modeling, and deliver reliable data features. Partner with Product, Engineering, and Data Science teams to translate retail signals into business insights. Establish standards for monitoring, documentation, reproducibility, and code quality. Use AI tools to accelerate development and prototype conversational data experiences for natural-language retail data exploration.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

Leads end-to-end machine learning delivery for insurance transformation programs, including feature engineering, model development, validation, tuning, explainability, governance, and production readiness. Translates business objectives into analytics use cases and delivery plans, manages client relationships, coordinates distributed teams, and supports architecture, UAT, MLOps, monitoring, and deployment discussions. Requires strong Python, statistical modeling, SQL, large-scale data, stakeholder communication, and production ML experience.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Partner with Finance, Engineering, Sales, Product, and Marketing to deliver reliable data for reporting and decision-making. Improve manual workflows, validate data accuracy, and design scalable solutions using modern data modeling practices. Support financial and revenue-related analytics, including ARR and revenue recognition, while balancing immediate fixes with long-term architecture.

Design, build, and scale real-time and batch data infrastructure and pipelines (Kafka, Databricks, Snowflake) to power analytics, reporting, and AI-powered data experiences. Drive data modeling, governance, observability, and best practices while partnering with cross-functional teams and mentoring engineers.

Leads Versapay’s enterprise data strategy, architecture, governance, quality, accessibility, and AI readiness. Builds unified data infrastructure, semantic models, catalogs, asset registries, and governance standards while enabling secure internal and commercial data products. Partners with Product, Engineering, Commercial, Finance, and Legal, and leads the Data Platform Team. Owns data maturity roadmaps, warehouse cost management, AI infrastructure readiness, and executive-level data strategy.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Labels, categorizes, validates, and quality-checks structured and unstructured data used to train AI and machine learning models. Maintains dataset accuracy, consistency, documentation, and version control while following detailed annotation guidelines. Collaborates with AI engineers, data scientists, and quality teams, identifies errors, provides process feedback, and supports continuous improvement of annotation workflows.

Leads a data engineering team responsible for large-scale streaming and batch ingestion pipelines, data transformations, and semantic data domains. Oversees architecture, implementation, reliability, observability, performance, and data quality improvements while mentoring engineers and driving delivery. Collaborates with product, engineering, support, and business teams, and promotes AI-assisted development and modern data architecture.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

Designs and deploys data integration, data management, reporting functionality, and data models for insurance-sector transformation projects. The role analyzes client needs, configures SAP solutions, applies data modeling expertise, and collaborates with software development and sales teams. Candidates need experience with IT transformation, insurance data processes, SAP FDS or SAP BW solutions, SAP HANA technologies, and technical mapping. The position is Canada-based with 50% travel and occasional on-site work in Calgary or Toronto.

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.