Top Data Jobs in Calgary

The Director of Data Strategy will develop and execute a data strategy, ensuring data quality and governance across the organization, fostering collaboration among various departments for improved analytics and business outcomes.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.

The Senior Data Scientist will build frameworks for data-driven decision making, develop predictive models, ensure data quality, and enhance customer experiences through analytics and operational insights.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead a team in developing scalable data solutions, mentor engineers, manage expectations, and contribute to code. Focus on high-quality software practices.

Responsible for designing, building, and maintaining scalable data pipelines using Databricks. Optimize ETL/ELT processes and ensure data quality, governance, and security.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

Lead enterprise Data & AI transformation initiatives, managing the delivery of cloud-based analytics platforms while aligning business and technology objectives. Responsibilities include program governance, stakeholder management, and overseeing implementations of Microsoft Fabric.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

Build and maintain scalable, reliable data systems and pipelines, uphold SLOs, partner with Product/Sales/Finance, enable self-service analytics, contribute to data platform strategy, and improve engineering practices for testing, reliability, and documentation.

Lead operational management and support of the enterprise data platform (primarily Snowflake), ensuring stability, scalability, observability, incident management, and migration from SQL Server. Manage data operations team, establish runbooks/SLAs, coordinate cross-functional readiness, drive continuous improvement, and ensure governance, compliance, and performance of data pipelines and platform services.

Own and drive the architecture and roadmap of ClickUp's data platform. Build scalable, reliable data pipelines and AI/ML infrastructure using AWS serverless, Snowflake, dbt, and Terraform. Lead cross-team technical initiatives, optimize cloud costs, establish engineering standards (observability, testing, CI/CD), mentor senior engineers, and influence org-wide architecture decisions.

Design and lead scalable enterprise data architectures using PostgreSQL and graph databases. Define logical and physical data models, optimize performance, modernize databases, integrate relational and graph systems, support AI/analytics platforms, produce governance documentation, and guide engineering teams through implementation.

Design end-to-end cloud-native AI and data platform architectures enabling Digital Twin and Knowledge Graph solutions. Lead architecture discussions, define scalable platform patterns, collaborate with AI/data/infrastructure teams, evaluate emerging AI technologies, produce technical documentation, and support solution discovery and roadmap planning.

Design and analyze experiments and causal studies, build retention measurement and predictive models, develop scalable PySpark/SQL data pipelines, create dashboards, and partner with product and growth teams to improve user engagement and retention.

Take end-to-end ownership of backend features and SaaS services in a microservices architecture. Design, implement, deploy, and operate scalable, reliable Node.js services. Work with Redis Streams, MongoDB, Kubernetes, and ensure test coverage, performance tuning, and collaboration with cross-functional teams.

Design and lead enterprise graph database and semantic data architectures, define ontologies and knowledge graph structures, integrate graph systems with cloud platforms, support LLM and AI use cases, run discovery workshops, document architecture, and mentor engineering teams.

Own go-to-market strategy and adoption for Stripe's data products. Develop positioning, messaging, competitive intelligence, launch plans, demand programs, and sales enablement. Translate customer and market insights into roadmap recommendations and measurable KPIs while partnering cross-functionally to drive commercialization and growth.

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.

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.

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.

Owner of Float's Snowflake architecture and ingestion layer: build orchestration, observability, data quality and contracts, CI/CD for data code, support ML pipelines, and collaborate with analytics, data science and product teams to deliver reliable, cost-efficient, governed data.