Top Data Jobs in Ottawa

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

Evaluate and rank multimodal model outputs, stress-test and document failures, author and edit dataset prompts/responses, build and apply rubrics, perform high-quality annotations across text, image, and structured formats, participate in calibration checks, adapt to evolving tasks, and report model performance trends to cross-functional teams.

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.

Design, build, and operate scalable cloud-native data platforms and production-grade pipelines using Azure, Microsoft Fabric, and Databricks. Implement lakehouse/medallion architectures, transform data with SQL/PySpark/Python, enable BI and AI workloads, enforce data quality, governance and security, automate deployments with DataOps/IaC, optimize costs, produce runbooks/docs, and mentor junior data engineers.

Design and maintain enterprise semantic models and AI-ready data products, build and optimize Power BI datasets and DAX metrics, enforce governance and security, enable self-service and AI-assisted analytics, and collaborate with stakeholders and data engineers to support BI and ML integrations.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

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.

In this role, you will define and execute the go-to-market strategy, drive growth, and lead customer success efforts at Reflow, a workplace intelligence platform.

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.

Perform accurate remote data entry: enter, update, verify, and correct records; organize digital files; process forms and invoices; ensure confidentiality and compliance; meet productivity and accuracy targets; collaborate with team to improve workflows.

Lead clinical data management from study start-up to database lock, oversee DM deliverables and teams, liaise with sponsors, manage timelines/budgets/scope, mentor staff, ensure GCP/SOP compliance, coordinate cross-functional biometrics activities, support audits, and drive process improvements and client satisfaction.

Lead design and implementation of enterprise Master Data Management: define MDM architecture, match/merge and survivorship rules, govern data quality and stewardship, design batch and real-time integrations with Databricks and operational systems, optimize platform performance, and mentor engineers while ensuring governance, lineage, and compliance.

Lead data architecture efforts: design relational and physical data models, develop and maintain ETL and reporting solutions, ensure data quality and governance, monitor data loads, support database performance and security, and translate business requirements into data designs.

Drive analytics and measurement for AI product adoption and impact. Build agentic analytics, small language models, predictive models, and customer-facing insights. Design and run experiments (A/B tests, causal inference), ship production models, and translate ambiguous business questions into actionable recommendations used by product, sales, and leadership.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Lead and grow SQUIRE's Data organization as a player-coach, owning long-term data architecture, building reliable Snowflake pipelines, setting standards for modeling and data quality, hiring and budgeting for a 5+ person team, partnering cross-functionally, auditing integrations, and enabling AI/LLM use cases while remaining hands-on for high-risk technical work.

Design and operate distributed inference systems for LLMs, build large-scale data pipelines, and debug production issues while collaborating with researchers and ML engineers.

The Product Data Analyst will analyze operational data, develop analytics frameworks, create dashboards in Tableau, ensure data integrity, and collaborate across teams to enhance decision-making and reporting.

Build and maintain core financial backend systems (trading, ledger, billing), transaction processing, reconciliation, and portfolio state management. Design and scale real-time and batch data pipelines, architect APIs and backend services, ensure reliability and observability, and contribute to distributed system design (event-driven, microservices, streaming). Partner with ML teams as needed.

Design, build, and operate cloud-native, serverless ClickHouse database infrastructure. Develop and maintain Kubernetes operators, improve metrics pipelines, support data ingestion/transformation (Spark/Kafka), collaborate with core DB and data plane teams, and ensure scalable, highly available distributed systems with production on-call responsibilities.

Manage interfaces and eligibility files for group insurance: perform daily data loading, validation and updates, document procedures, communicate with clients and stakeholders, and recommend process improvements and automation.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Design, build, and maintain company-wide Snowflake data assets and dbt data models. Define metrics and dimensions, implement reliable pipelines, enable self-service analytics, collaborate with stakeholders, ensure data quality, and document solutions to support reporting, dashboards, and analysis.

Lead design, build, and operate scalable data platform and pipelines (ETL/reverse ETL). Own architecture for AWS/Snowflake data lake, optimize ingestion, enforce data quality and governance, introduce AI-driven engineering practices, mentor engineers, and drive cross-functional data projects from requirements through production.