Top Data Jobs in Calgary

Leads and develops an enterprise sales team responsible for strategic seller acquisition, pipeline generation, complex deal execution, forecasting, and revenue outcomes. Builds repeatable sales motions, coaches Enterprise Account Executives, manages territory and account strategies, leads high-value negotiations, and partners cross-functionally with product, legal, finance, marketing, implementation, and customer success teams. Uses market insight to influence product, pricing, partnerships, and go-to-market strategy.

Leads and develops an enterprise sales team responsible for strategic seller acquisition, pipeline generation, complex deal execution, forecasting, revenue growth, and repeatable upmarket sales motions. Partners cross-functionally with product, solutions engineering, legal, finance, marketing, partnerships, implementation, and customer success. Coaches enterprise account executives, manages strategic opportunities, improves deal quality, and uses market insight to influence product, pricing, and go-to-market strategy.

Owns Pfizer GH&B’s AI-ready Snowflake data warehouse, data architecture, ontologies, governance, quality, lineage, access controls, and production reliability. The role supports AI/ML models, dashboards, and business-critical data applications; establishes testing, release, monitoring, and incident processes; directs contingent technical resources; and aligns business-specific data decisions with enterprise standards. Requires strong SQL, Python, Collibra, data warehousing, unstructured-data architecture, ontology, governance, and stakeholder-influence experience.

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.

Own and deliver highly available, low-latency backend identity systems supporting consumer onboarding, authentication, and verification. Lead quarterly team goals, guide engineers through complex problems, collaborate with product and design stakeholders, improve technical and code quality standards, monitor system availability, support on-call operations, and mentor team members.

Serve as the technical anchor for Domino’s identity and authorization platform. Design scalable, relationship-based authorization using SpiceDB; guide migration from legacy access checks; build cloud-native identity integrations; improve platform reliability, security, and architecture; and advise engineering teams on authorization, APIs, abstractions, and AI-assisted development practices.

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.

Designs, builds, and optimizes production-grade data pipelines using Azure, Databricks, Delta Lake, and Apache Spark. Develops ETL and data integration solutions, automates ingestion and validation, and ensures pipeline reliability, security, and compliance. Leads client data engineering engagements, translates business requirements into scalable architectures, collaborates with stakeholders, mentors junior engineers, and supports analytics and machine learning initiatives.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

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.

Develop and own production-grade data products and machine learning models across fraud, credit risk, payments, marketing, and internal operations. Responsibilities include end-to-end model development, deployment, monitoring, data visualization, MLOps collaboration, stakeholder partnership, and model performance optimization. The role also mentors colleagues, establishes data science standards, and helps scale the organization’s technical capabilities.

Owns enterprise data governance and quality practices, including stewardship, ownership, governance councils, data quality rules, dashboards, issue remediation, and Master Data Management. Supports responsible AI governance by coordinating assessments, documenting risks and controls, and aligning use cases with organizational guardrails. Partners with business, technical, security, privacy, legal, risk, and architecture teams while communicating progress and business impact to leadership.

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.

Develop and productionize fraud detection machine learning models using large-scale behavioral, device, network, and transaction data. Lead investigations into complex fraud attacks, reconstruct attacker behavior, identify trends, and produce technical reports and customer-facing insights. Collaborate with product, engineering, operations, legal, executives, and customers to improve detection systems and automate analytic workflows.

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.

Design, develop, and support business applications, automations, and AI-enabled solutions. Build full-stack applications, prototypes, and production systems using modern cloud, DevOps, and generative AI technologies. Collaborate with stakeholders, architects, data teams, and infrastructure teams to deliver secure, reliable solutions. Support applications throughout their lifecycle, contribute to engineering standards, troubleshoot production issues, and mentor developers within Agile delivery processes.

Manage real-time drilling data connections and streaming quality control for active drilling operations. Load, normalize, validate, and clean drilling data; troubleshoot communication, telemetry, and transmission issues using WITSML and related protocols. Support client trials, coordinate with MWD vendors and data providers, and collaborate with software, directional drilling, and technical support teams. The role is office-based and suited to professionals transitioning from rig-site MWD or directional drilling experience.

Develops revenue analytics, business intelligence solutions, data models, automated pricing and inventory tools, reports, and decision-support systems. Translates Revenue Management and Pricing business problems into actionable insights, supports system migrations and administration, conducts testing, automates routine processes, and represents the business on cross-functional projects. The role requires strong SQL, database, OLAP, ETL, communication, and project coordination skills.

Build a functional prototype addressing a real RBC business challenge within a collaborative summer innovation program. Responsibilities include data transformation, engineering, cleaning, mining, product design, troubleshooting, development, stakeholder collaboration, research, strategy, communications, and executive presentations. Participants work with mentors and subject matter experts while applying data science, programming, agile development, and design-thinking techniques.

Develop machine learning models and detection rules for web security products. Analyze large datasets to distinguish legitimate from fraudulent traffic, improve detection accuracy, support production data workflows, troubleshoot distributed systems, and provide insights that guide product improvements and strategy.

Build and operate scalable analytics data platforms using Java, Python, Spark, Airflow, Iceberg, and AWS. Responsibilities include developing batch pipelines, automated testing frameworks, CI/CD validation, data quality controls, representative test datasets, and release gates. The role also advances AI-driven engineering workflows, supports production reliability through on-call participation and incident analysis, and collaborates with distributed engineering teams to improve platform quality, scalability, and observability.

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.