Top Data Jobs in Calgary

Leads strategic pricing and monetization across sales-driven channels, managing pricing partners and advising on complex commercial deals. Develops pricing frameworks, approval processes, deal governance, and scalable tools using automation, AI, and data. Partners with Sales, Product, Engineering, Legal, Risk, and Revenue Operations to improve deal economics, profitability, pricing performance, packaging, and portfolio-level monetization strategy.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Build scalable AI and machine learning platform capabilities across model development, deployment, monitoring, cataloging, tagging, and LLM hosting. Collaborate with customers and cross-functional teams to integrate platforms such as AWS SageMaker and Azure ML. Develop secure APIs and distributed backend systems, optimize cloud and containerized services, and establish robust testing and CI/CD pipelines.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Designs and maintains reliable data pipelines and computed tables using SparkSQL and PySpark. Integrates IoT, product, customer, external, unstructured, and ML-related data into scalable data models supporting analytics, causal inference, model training, and dashboards. The role requires ETL development, data orchestration, API integration, strong data quality and uptime, and collaboration with Data Science, AI/ML, Analytics, and engineering teams. This is a remote position for candidates residing in Canada.

Own the vision, roadmap, and end-to-end data manufacturing process for Power Infrastructure datasets, including substations, transmission lines, and grid infrastructure. Partner with engineering, analysts, customers, and energy experts to identify data sources, define enrichment and validation logic, improve data quality, and deliver scalable automated pipelines. Establish metrics for accuracy, coverage, freshness, and reliability while serving as the internal product expert and driving initiatives from discovery through production.

Build and scale reliable Snowflake data pipelines, ML pipelines, monitoring systems, and AI-driven agentic tooling that automates pipeline generation, testing, and maintenance. The role partners with analysts, data scientists, product teams, and analytics engineers to deliver scalable end-to-end data infrastructure for Babylist’s e-commerce platform.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

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.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

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.

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.

Leads enterprise data and AI governance by designing frameworks, controls, standards, operating models, and responsible-use practices. Oversees data quality, metadata, lineage, lifecycle, classification, AI risk, assessments, compliance, reporting, tooling, literacy, and adoption. Partners with Legal, Privacy, Cybersecurity, Technology, and business teams while managing vendors and developing a high-performing governance team.

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.

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.

Leads end-to-end UX design for complex, data-dense enterprise workflows and applications. Responsibilities include managing a code-first design system, defining interaction patterns, creating high-fidelity Figma and coded prototypes, conducting user research, and partnering with product, engineering, and domain experts. The role also establishes design standards, reviews implementation quality, coaches colleagues, and incorporates AI tools into design and prototyping workflows.

Develops and supports cloud-based data pipelines, integrations, data products, reporting layers, and analytics capabilities within FCC’s AWS data ecosystem. Translates business requirements into scalable technical solutions, troubleshoots and optimizes data systems, and contributes to testing, documentation, security, and continuous improvement. Provides technical leadership and mentorship while collaborating with analysts, product teams, technology partners, and business stakeholders.

Designs and demonstrates secure, scalable Azure database and analytics solutions for enterprise customers. Leads technical sales engagements, proofs of concept, hackathons, architecture workshops, migrations, and production deployments. Advises platform leaders, resolves technical blockers, collaborates with engineering, and develops expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, on-premises data warehouses, Hadoop, and competing analytics platforms.

Analyzes and improves data quality across enterprise systems within the Data Governance team. The role investigates complex data models and master data issues, performs root cause analysis, develops business-focused quality rules, translates steward requirements into technical specifications, and recommends remediation plans. It also documents findings, supports governance policies, and creates reports and dashboards for technical and non-technical stakeholders while adopting tools such as Soda, Microsoft Purview, and Databricks.

Supports financial management and governance of D&IT investments through budgeting, forecasting, financial modeling, business cases, total cost of ownership analysis, and benefits realization. Uses SAP financial data to analyze costs, commitments, actuals, and trends. Produces executive reporting and governance materials while partnering with Finance, technology, project, and business teams to improve portfolio prioritization, decision-making, and investment value.

Owns enterprise data-services product roadmaps, backlogs, specifications, prioritization, delivery, adoption, and success metrics. Partners with UX, engineering, AI, data, and energy-domain experts to transform advisory workflows and deliver secure, integrated, AI-enabled platform capabilities. Leads discovery, cross-functional delivery, rollout, stakeholder communication, and continuous improvement across global user groups.

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.

Leads the Liquids Pipelines data modernization roadmap, delivering scalable data pipelines, curated datasets, analytics, dashboards, automation, and AI-enabled solutions. Translates business and operational needs into technical designs, data models, governance practices, and multiyear delivery plans. Focus areas include power cost optimization, operational performance, data quality, and executive reporting. Partners with stakeholders and platform teams while mentoring analysts and advisors and guiding solutions from concept through sustainment.