Top Data Jobs in Ottawa

Lead and grow a Data Engineering team to build production data pipelines, medallion datasets, and reusable data products. Drive architecture, engineering standards, design reviews, and cross-functional delivery. Partner with Product, Analytics, Data Science, and Platform to enable governed, reliable data foundations for analytics and AI. Mentor engineers and manage multiple concurrent initiatives to deliver predictable outcomes.

Designs, develops, and optimizes enterprise data solutions, including data models, integrations, pipelines, warehouses, analytics platforms, governance, security, and quality management. Translates business requirements into scalable technical solutions, supports reporting and self-service business intelligence, monitors platform performance, and provides technical guidance to stakeholders and teams.

This role involves deploying solutions for clients, managing data compliance, and providing feedback for product improvement. Requires collaboration with teams and clients.

Build scalable GCP-based data pipelines and datasets for insurance fraud analytics. Develop and evaluate machine learning, NLP, and Generative AI models using claims and unstructured SIU data. Apply explainable AI techniques, conduct exploratory analysis, automate data workflows, document architectures, and support privacy, fairness, and compliance requirements while collaborating with investigators and business analysts.

Develop and deploy machine learning models that improve e-commerce search relevance, recommendations, product classification, and customer intent understanding. Responsibilities include collecting and preparing large datasets, feature engineering, predictive modeling, validation, testing, and leading work in areas such as NLP, computer vision, information retrieval, and deep learning. The role requires building scalable, reusable machine learning pipelines and collaborating across concurrent projects.

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.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

The Software Engineer will manage data collection for model training operations, enhance cloud infrastructure, and collaborate with AI scientists to improve data quality and scale.

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 programs at Syneos Health, supporting drug development and commercialization services. The role operates within a life sciences clinical solutions environment and contributes to accurate, compliant, and high-quality trial data review. Specific day-to-day tasks and qualification requirements are not provided in the job description.

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.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

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.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Implement and configure enterprise Workday HCM solutions, lead the data workstream including gathering, transforming and loading organizational and employee data, liaise with customers and internal stakeholders, deliver data solutions, support sales and practice improvements, and educate others within the Workday practice.

Lead single-threaded product and technical ownership for Client Intelligence & Data, defining APIs and event-driven architectures to deliver merchant-facing analytics, KPIs, and reports. Drive platform strategy toward AI-driven insights, enforce production reliability and automated QA, champion You Build It You Run It culture, partner cross-functionally to scale multi-tenant analytics capabilities, and prioritize work using evidence-based frameworks.

Design and implement data models, pipelines, and ML systems to power AI agents and analytics. Build, deploy, and monitor machine learning models at scale, integrate Snowflake/AWS and Salesforce Data Cloud, and deliver production-ready data products and views to stakeholders.

Lead data management for clinical trials including database setup, eCRF specification, validation/edit checks, testing, data review/reconciliation, SAE reconciliation, database lock/unlock, vendor data transfers, training, and DM documentation to meet timelines and quality standards.

Lead optimization and scaling of Snowflake architecture, build resilient ELT/ETL and MLOps pipelines, implement data observability, automate workflows with AI-assisted tools, and design enterprise semantic models to support analytics and commercial data products.

Perform high-level video captioning, action-based timeline segmentation, and visually-verifiable action labeling. Review and correct AI-generated captions, meet accuracy benchmarks, follow strict guidelines, and complete certification.

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.