Top Data Jobs in Calgary

Design and build scalable backend systems, APIs, data models, event-driven services, experimentation platforms, and personalization systems. Partner with Product, Data Science, Analytics, and Design teams to support customer acquisition and engagement. Establish secure, observable, high-performance engineering practices, mentor junior engineers, and participate in monitoring, on-call, and operational support.

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.

Lead data science initiatives for Risk and Support Operations by analyzing customer and product data, defining metrics, forecasting demand, evaluating experiments and risk policies, and building decision frameworks. Use AI tools and agent workflows to improve analytical efficiency. Partner with product, engineering, and risk teams, communicate insights to senior stakeholders, establish technical standards, mentor data scientists, and support hiring.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, workflow automation, evaluation, and observability capabilities. Work hands-on with Python, SQL, dbt, lakehouse data, cloud infrastructure, model selection, and inference-cost optimization. Establish reusable engineering patterns and mentor teams through high-quality code and system design.

Analyze product, customer, risk, and support data to guide decisions across Block’s platforms. Own metrics, forecasting, staffing models, experiments, visualizations, and measurement frameworks. Evaluate risk models and policies for performance, bias, and drift; use AI tools and agent workflows to improve analytical execution. Partner with product, engineering, and risk stakeholders, lead technical standards, build reusable tooling, mentor data scientists, and contribute to hiring.

Drive strategic federal sales growth across DISA 4th Estate and Intelligence Community accounts. Responsibilities include pipeline development, account planning, opportunity and win strategy, stakeholder relationship development, acquisition navigation, RFI and solicitation support, CRM management, forecasting, and translating technology solutions into mission outcomes. The role requires expertise in long-cycle federal sales, procurement processes, secure environments, and technology solutions.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Leads product marketing strategy, positioning, messaging, go-to-market execution, AI adoption, product launches, market expansion, and customer-driven roadmap input. Builds and manages a high-performing product marketing team, partners with executive, product, revenue, and marketing stakeholders, and drives product adoption, retention, category growth, and revenue across programmatic advertising and martech markets.

Build and maintain scalable, tested data models using SQL, dbt, and cloud data warehouses. Partner with Product, Revenue Operations, Finance, Data Engineering, and other stakeholders to translate business needs into trusted datasets. Establish data quality monitoring, documentation, governance, and modeling standards while supporting analytics, reporting, self-service data use, and AI-ready products. Use AI-powered development tools to improve productivity and code quality.

Lead and develop five experienced analysts and data scientists while personally owning analytics across ecommerce, health, and supply chain. Partner with executives, establish KPI and reporting standards, prioritize the analytics roadmap, deliver strategic insights, and advance AI-native self-service analytics tools. The role combines people leadership, hands-on analysis, data governance, and business strategy in a remote-first environment.

The Data Scientist develops predictive and prescriptive models, machine learning algorithms, simulations, anomaly detection systems, and A/B testing frameworks. They analyze structured and unstructured data, identify business opportunities, support product and process improvements, monitor model performance, and communicate insights through reports and visualizations. The role involves end-to-end analytics delivery, collaboration with stakeholders and data engineers, data governance, and influencing executive-level decision-making.

Leads end-to-end data science and machine learning initiatives, from problem discovery and proof of concept through production deployment and continuous improvement. Develops statistical, optimization, simulation, and machine learning solutions using strong software engineering, MLOps, and Responsible AI practices. Partners with stakeholders and multidisciplinary teams, provides technical direction, evaluates feasibility and risks, communicates analytical recommendations, and remains hands-on in solution development.

Leads institutional data governance, quality, management, and analytics initiatives. Responsibilities include data profiling, quality investigations, remediation, metadata and glossary management, mastering, governance controls, stakeholder requirements, Ataccama One support, dashboards, reporting, risk analysis, and data literacy. The role collaborates with data stewards, trustees, engineers, and business stakeholders to establish standards, resolve data issues, improve governance processes, and ensure accurate, compliant, decision-ready data.

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.

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Build and productionize statistical and ML models for Toast's restaurant platform. Partner with engineers, product, and business teams to mine data, design experiments, evaluate models, and communicate actionable insights that drive product growth and customer outcomes.

Leads enterprise architecture strategy for AI, data platforms, analytics, and governance. Designs scalable lakehouse, AI/ML, streaming, integration, BI, and data governance solutions using cloud technologies. Establishes architecture standards, leads reviews and transformation initiatives, guides build-versus-buy decisions, and ensures security and compliance alignment. Partners with executives and cross-functional technology teams, mentors architects, and drives standardization, innovation, performance, and solution quality across AI and data ecosystems.

Leads enterprise architecture strategy for AI, data platforms, analytics, and governance. Designs scalable lakehouse, AI/ML, streaming, integration, and BI ecosystems across cloud platforms. Establishes architecture standards, governance, security, and compliance practices; guides technology investments and transformation initiatives. Partners with executives and cross-functional engineering, data science, BI, and platform teams, while mentoring architects and driving standardization, innovation, performance, and solution quality.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, forecasting, targeting, and personalization. Collaborate with engineers and product teams, set modeling and productionization standards, mentor data scientists, guide architecture and experimentation, and apply cloud and MLOps best practices to drive measurable product and business impact.

Lead end-to-end data science initiatives: design, deploy, and monitor ML/statistical models for credit risk, pricing, collections and fraud. Collaborate cross-functionally to integrate models into production, define model development standards, mentor team members, and maintain scalable ML pipelines and monitoring systems to drive measurable business impact.

Analyzes pipeline, funnel conversion, campaign performance, retention, and revenue drivers across Sales, Marketing, and Client Success. Builds and maintains dbt models, data marts, quality controls, documentation, and BI-as-code assets using Salesforce and modern data-stack technologies. Partners with stakeholders to turn ambiguous questions into decisions and advances AI-agent workflows for faster analytics and reporting.