Top Data Jobs in Ottawa

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.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

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.

Analyze and enter building asset data, support AssetPlanner software implementation, configure databases, assist clients, conduct quality assurance reviews, prepare reports, and provide onboarding and training. The role also handles support requests, client interviews, software testing, bug reporting, and enhancement recommendations. Candidates need a university degree, strong Excel and MS Office skills, communication abilities, and the capacity to learn new software quickly. Facilities management, building science, engineering, asset management software, and client service experience are advantageous.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, strong SQL or Python skills, persuasive communication, and ownership of ambiguous problems with measurable business impact.

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; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independently structuring data, solving ambiguous problems, communicating recommendations clearly, and delivering measurable business impact.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, SQL or Python proficiency, strong communication, and ownership of ambiguous initiatives through measurable business impact.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent data work, strong analytical reasoning, persuasive communication, and ownership of ambiguous problems through measurable business impact.

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 stakeholders and leadership decisions. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems through measurable outcomes.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions and outcomes. The role requires independently structuring and interpreting data, communicating findings to leadership, and solving ambiguous problems using SQL or Python.

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.

As a Product Manager at Reflow, you will lead product strategy, execution, and delivery while ensuring customer engagement and managing engineering dependencies in an early-stage startup environment.

The Director of Product Management will lead product strategy across infrastructure, software enablement, and customer-facing platforms, enhancing both technical and user experience while managing a team of product managers.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Owns the design, delivery, and sustainment of infrastructure, tooling, automation, and CI/CD for Assent’s Data and AI Platform. Builds scalable AWS infrastructure supporting Snowflake, dbt, Airflow, data pipelines, warehousing, orchestration, and AI/ML workloads. Partners with Data Engineering, establishes observability and incident response practices, governs access and security, and guides deployment and infrastructure best practices while contributing to broader DevOps standards.

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 data engineering teams and oversees the full lifecycle of cloud-based data solutions, including architecture, ETL/ELT pipelines, testing, deployment, monitoring, governance, and operational support. Partners with business and technology stakeholders to translate requirements into curated data products. Manages delivery timelines, budgets, vendors, technical reviews, and risks while mentoring engineers and analysts and driving reliability, security, documentation, and continuous improvement.

Curate, validate, and maintain scientific datasets and metadata for Wiley products, databases, APIs, and software platforms. Assess scientific content quality, acquire data from internal and external sources, develop editorial priorities, and collaborate with product, engineering, scientific, and commercial teams. The role also improves data quality, editorial processes, and content scalability while supporting customer workflows and product releases.

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.

Lead design and rollout of a fit-for-purpose data governance program: manage data lifecycle, implement governance frameworks and tools, drive data catalogs/lineage, upskill stewards, define KPIs, and partner cross-functionally to improve data quality, compliance, and usability.

Manage and optimize high-load Microsoft SQL Server databases, including advanced T-SQL development, query and execution-plan optimization, indexing, locking, memory, disk performance, AlwaysOn availability, maintenance, recovery, security, integrity checks, monitoring, and incident resolution. Support analysts and ML engineers with reliable data access across databases reaching tens of terabytes.

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.

Staff Data Scientist responsible for owning ambiguous, high-impact product and business problems; designing experiments; analyzing product data; developing analytical tools and frameworks; partnering with product, engineering, design, and leadership; and mentoring data scientists. The role requires advanced statistical expertise, strong product judgment, production programming experience, and the ability to translate complex analyses into strategic recommendations.

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.

Develop and deploy statistical and machine learning models for credit risk, fraud, collections, and marketing. Responsibilities include data exploration, model development, production monitoring, performance analysis, governance, stakeholder collaboration, and communicating insights to leadership. The role requires applying advanced statistics and machine learning to improve consumer finance strategies while maintaining documentation, quality assurance, and version-control standards.