Top Data Jobs in Ottawa

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Design, build, and scale backend data services, APIs, microservices, distributed systems, and high-volume data pipelines. Optimize PostgreSQL and Oracle databases, improve reliability and performance across regions, diagnose complex production issues, and mentor engineers. Partner with DBA and SRE teams while providing hands-on technical leadership and owning services end to end.

Analyze large datasets to produce dashboards, metrics, and insights supporting product, care, and business decisions. Build BI infrastructure, run A/B and multivariate tests, perform ad-hoc analyses and visualizations, and collaborate with stakeholders and data scientists to inform strategy and operations.

Leads the enterprise data engineering capability, including architecture, integration patterns, cloud data platforms, scalable pipelines, APIs, data governance, and real-time processing. Establishes standards and roadmaps across Azure, Snowflake, and MuleSoft while enabling business intelligence, automation, machine learning, RAG, vector search, and AI initiatives. Collaborates with stakeholders and technical teams to deliver governed, observable, high-quality enterprise data solutions.

Leads enterprise data engineering and AI platform strategy, including Azure, Snowflake, and MuleSoft architectures; scalable pipelines, APIs, data lakes, warehouses, governance, integration, real-time processing, and AI capabilities such as RAG and vector search. Partners with stakeholders to deliver trusted data products supporting analytics, automation, reporting, machine learning, and enterprise AI initiatives.

Supports client engagements involving data de-identification and re-identification risk measurement, particularly for healthcare data. Responsibilities include validating and preparing datasets, conducting qualitative and quantitative risk analyses, documenting findings in client reports, preparing presentations, supporting solution delivery, coaching clients, and developing expertise in statistical disclosure control, privacy regulations, and de-identification standards.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Owns the technical and statistical integrity of payment data analytics workflows. Responsibilities include SQL and Python data ingestion, transformation, validation, quality controls, feature engineering, time-series analysis, model readiness, back-testing support, GCP analytics environment management, workflow automation, documentation, and translating findings for product and executive stakeholders.

Collects, extracts, analyzes, interprets, and validates data to support business decisions. Develops and maintains management indicators, reports, data analytics solutions, quality-control procedures, documentation, and training materials. Participates in SAS ETL development, supports data governance and publication quality, and assists users with data requests and interpretation.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Develop and optimize secure networking software for Ribbon’s next-generation products across cloud, server, and custom hardware platforms. Responsibilities include data-plane acceleration using network processors, debugging customer issues, collaborating with system testing, and measuring product performance. The role requires strong C and embedded programming experience, networking expertise, Linux and kernel knowledge, and familiarity with technologies such as DPDK, VPP, SmartNICs, Kubernetes networking interfaces, tunneling, and eBPF.

Analyze the customer lifecycle across activation, engagement, retention, expansion, and renewal for a product-led SaaS business. Partner with Customer Success, Product, Growth, RevOps, and Data Engineering to develop analytical frameworks, identify growth opportunities, evaluate experiments, and provide actionable recommendations to senior leaders. Build data foundations and standards supporting digital success, AI, automation, forecasting, anomaly detection, and generative BI.

Conduct independent research on machine learning, knowledge representation, formal methods, AI agents, and trustworthy AI systems. Develop algorithms, models, prototypes, benchmarks, and rigorous experiments for enterprise-scale applications. Collaborate with scientists, engineers, and domain experts to translate operational constraints into deployable research systems. Evaluate reliability, robustness, privacy, interpretability, latency, cost, and process outcomes, while communicating findings through publications and internal or external presentations.

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.