Top Data Jobs in Ottawa

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Work full time on a defined data science and analytics project using production robot, retail, and operational data. Responsibilities include SQL and Python analysis, BigQuery and dbt workflows, dimensional modeling, dashboard development, data-quality validation, analytical insights, and collaboration with technical and business stakeholders. Interns present their project and results at the end of the program.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Build data and evaluation systems for Glean’s AI products, including benchmark datasets, scalable eval infrastructure, agent observability, LLM-based verifiers, CI and production evaluation pipelines, and enterprise simulation environments. Analyze traces and failures, measure quality with statistical rigor, and translate findings into product improvements. Partner with research, engineering, product, and go-to-market teams to improve agent performance while balancing quality, latency, and cost.

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.

Designs, builds, and maintains scalable batch and streaming data pipelines across AWS and GCP. Develops ETL/ELT workflows using Python, PySpark, and advanced SQL; optimizes performance, storage, and query execution; implements data quality and governance controls; and supports data lakes, warehouses, and ML workflows. Collaborates with data scientists and ML engineers on MLOps automation and production deployments while maintaining CI/CD, Docker, infrastructure standards, and technical documentation.

Leads data integration development using Azure, Hadoop, Spark, Scala, Java, SQL, and relational databases. Responsibilities include data modeling, analytics, solution design, requirements analysis, development, maintenance, and quality delivery in an agile, client-focused environment. The role requires strong problem-solving, interpersonal, communication, and technical skills, along with adaptability, motivation, and the ability to learn quickly.

Leads hands-on data integration and engineering initiatives using Azure Databricks, Azure Data Factory, PySpark, Python, and SQL. Modernizes legacy on-premises systems into cloud-native architectures, implements Medallion Architecture and data warehousing, improves pipeline reliability and compute efficiency, develops CI/CD workflows, and mentors engineering teams delivering secure, actionable analytics for banking environments.

Leads hands-on data integration and engineering initiatives using Azure Databricks, Azure Data Factory, PySpark, Python, and SQL. Modernizes legacy on-premises systems into cloud-native architectures, implements Medallion Architecture and data warehousing, improves pipeline reliability and compute efficiency, establishes CI/CD workflows, supports banking-grade security, and mentors engineering teams delivering actionable analytics.

Lead real-time data integration using Azure Databricks, PySpark, Kafka, ADF, and ADLS. Build and optimize medallion-style ETL pipelines processing over 10 million daily records. Improve transformation efficiency, data quality, validation, and downstream reliability by identifying upstream data issues, implementing root-cause fixes, and collaborating with stakeholders.

Own product experimentation and causal analysis for growth and engagement initiatives. Define KPI frameworks, evaluate retention and user behavior, build predictive models for lifecycle interventions, and assess segmented and long-term effects. Partner with Product, Engineering, and Design to recommend launches, iterations, or stops. Translate ambiguous questions into actionable insights, influence product priorities, and create reusable analyses using Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS.

Own company-wide KPI, modeling, and measurement workstreams. Build trusted metric frameworks, KPI trees, forecasts, scenarios, causal analyses, and reproducible SQL/Python workflows connecting product drivers to retention, monetization, revenue, and margin. Partner with Data Engineering, Product, Engineering, Finance, and Commercial teams to define certified metrics, build analytical assets, drive adoption, and communicate assumptions, sensitivity, uncertainty, and actionable recommendations.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes governed self-service analytics, and partners with Finance, Product, Commercial, Engineering, and Data Engineering. The role drives trusted metric adoption, improves decision speed, mentors scientists, and aligns leadership on targets, trade-offs, uncertainty, and investment decisions.

Leads a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models and systems. Owns technical direction, causal inference frameworks, predictive modeling, experimentation, and productionization. Mentors scientists, partners with product, engineering, sales, and business development, communicates methodology to customers and executives, and establishes scalable standards for measuring advertising impact.

Own the technical direction and delivery of WEX’s data acquisition platform. Build reusable, high-performance batch, CDC, and streaming ingestion services with schema validation, reconciliation, recovery, lineage, security, observability, and auditability. Lead architecture and code reviews, establish testing and CI/CD practices, mentor engineers, resolve cross-team dependencies, and support production escalations. The role requires hands-on development of scalable distributed systems and configuration-driven connectors.

Lead data science strategy for Fetch’s Play and Rewarded Apps business. The role develops predictive and machine learning models, designs experiments and causal analyses, builds scalable analytical workflows, and connects user behavior to revenue, engagement, retention, and marketplace outcomes. This senior individual contributor partners with Product, Marketing, Engineering, and executives, operationalizes analytical solutions, communicates recommendations, mentors data scientists, and establishes best practices for rigorous modeling, experimentation, documentation, and data storytelling.

Owns the lifecycle of international data products from business requirements through engineering delivery, release, and production monitoring. The role investigates data issues using SQL and Python, validates data quality, defines acceptance criteria, manages requirements and traceability, and translates findings into actionable business insights. It partners closely with stakeholders and data engineering teams to resolve incidents, improve data trust, and establish monitoring and stewardship practices.

Build and optimize data pipelines, applications, ETL processes, data models, warehouses, integrations, and reporting solutions for financial services clients. The role analyzes requirements, performs data profiling and source-to-target mapping, supports cloud and data migration initiatives, documents technical approaches, collaborates with client and project teams, contributes to internal accelerators, and mentors junior consultants.

Leads the enterprise data platform engineering team and owns its reliability, security, scalability, performance, cost, and operational support. Oversees relational, NoSQL, and analytical platforms; data movement and transformation; infrastructure as code; GitOps; database automation; high availability and disaster recovery; monitoring; compliance; and incident response. Manages engineers, on-call operations, technology selection, vendor evaluations, budgeting, and cross-functional delivery while remaining hands-on in architecture, administration, performance tuning, and complex technical escalations.

Owns partner marketing operations across Salesforce reporting, campaign data, MDF budgeting and reconciliation, partner portal administration, training content, and system integrations. The role analyzes partner engagement and campaign performance, maintains accurate records across platforms, troubleshoots portal issues, manages partner access, and coordinates with Marketing, Sales, Finance, IT, and external partners. Success requires strong organization, project management, data analysis, Salesforce, Excel, and PRM platform experience.

Build and validate transformed data, develop dbt models, manage Airflow scheduling, and support data pipelines. Analyze data and present recommendations to stakeholders, ensure insight accuracy, implement data exploration tools, and mentor team members. The role requires collaboration across business and engineering teams, strong SQL expertise, and experience with BigQuery, dbt, Airflow, Python, and relational databases.

Sets the technical vision for data science, leads complex credit risk, pricing, and loss forecasting models from conception through production, and architects scalable machine learning pipelines. Partners with senior stakeholders to shape strategy, establishes modeling and validation standards, mentors technical staff, and communicates recommendations to executives. The role requires deep fintech lending experience, advanced statistical and machine learning expertise, and hands-on production model deployment.

Designs, builds, automates, and optimizes enterprise cloud data pipelines and platforms. Responsibilities include ETL/ELT, DataOps, CI/CD, data migration, data modeling, data warehousing, lakehouse architecture, database administration, performance tuning, and production support. The role uses Databricks, Azure services, PySpark, SQL, and multiple cloud platforms to deliver scalable structured and unstructured data solutions.

Lead the technical strategy and multi-year roadmap for Data Labs, overseeing AI benchmarking systems, data pipelines, and reinforcement-learning environments. Manage engineering managers, forward-deployed engineers, and senior individual contributors while partnering with research, product, commercial, and executive teams. Guide platform investment, evaluation methodology, market positioning, client engagement, organizational growth, and architectural decisions supporting AI training and deployment.

Develop customer-specific AI solutions for consequential business decisions. Investigate internal and public information, evaluate evidence, identify uncertainty, and design reliable AI workflows. Work directly with customers to understand needs, prototype solutions, test them with users, and improve their usefulness. Translate successful workflows into repeatable product capabilities while partnering with engineers on integrations and missing technical functionality.