Top Data Jobs in Calgary

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.

Lead enterprise client reporting and manage Proof’s data team. Own report accuracy, delivery, client relationships, data pipelines, shared models, BI tooling, and internal data insights. Coach team members, guide technical decisions, review SQL and data models, partner with Engineering and Product, resolve reconciliation issues, and improve automation. The role will establish data quality standards and help the company use trusted data to support growth, internal tools, and AI systems.

Leads company-wide measurement and causal strategy, defining canonical metrics, semantic data architecture, experimentation standards, and strategic models such as marketing mix, elasticity, incentive sensitivity, and forecasting. Partners with engineering and analytics teams to build scalable, production-ready analytical frameworks, improve scientific rigor, and guide executive decisions. Provides organization-wide technical leadership, develops modeling standards, and applies advanced statistical and causal methods to high-impact business problems.

Own the scientific and product roadmap for Fetch’s monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead methodology, evaluate evidence, balance reach, precision, revenue, and cost, and convert bespoke studies into scalable products. Partner with Product, Engineering, Sales, and GTM on pilots, positioning, and customer value. Remain hands-on with SQL, Python, statistical modeling, and causal inference while mentoring scientists and influencing senior stakeholders.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Own the data science strategy, analytical roadmap, and experimentation for ranking and relevance. Partner with engineering, product, commercial, and offer teams to improve advertiser outcomes, user value, retention, and marketplace economics. Design rigorous experiments and observational analyses, quantify incremental business impact, establish KPI frameworks and trade-off guardrails, influence launch and investment decisions, mentor colleagues, and build reusable analytical methods using large-scale data systems.

Owns the roadmap and product strategy for private markets, shareholder, and deals financial data. Responsibilities include identifying content gaps, defining requirements and PRDs, improving data quality and discoverability, collaborating with platform, engineering, design, and go-to-market teams, conducting QA, and driving adoption among financial analysts, investment bankers, and corporate development professionals.

Own the product roadmap for surfacing financial data through search, screening, and agentic capabilities. Define requirements, PRDs, and prioritization; collaborate with engineering, design, data platform, AI/ML, and go-to-market teams; and lead feature development from discovery through launch. Improve content structure, tagging, indexing, ranking, search relevance, screening precision, and agentic retrieval reliability for financial analysts, bankers, and corporate development users.

Supports data extraction, management, quality assurance, cleaning, validation, harmonization, statistical and qualitative analysis, and public health program evaluation. Develops analytic datasets and contributes to technical memoranda, reports, presentations, tables, and figures. Analyzes implementation measures and mixed-methods data, interprets findings, and recommends program improvements. Requires collaboration with government and public health stakeholders, use of Microsoft Office and CDC administrative systems, and the ability to obtain and maintain required customer clearance.

Design, build, and scale backend data services, APIs, microservices, distributed systems, and high-volume data pipelines. Optimize PostgreSQL and Oracle databases, improve reliability and performance across regions, diagnose complex production issues, and mentor engineers. Partner with DBA and SRE teams while providing hands-on technical leadership and owning services end to end.

Leads advanced data analysis, develops dashboards, KPIs, and analytical models, and translates business problems into data-driven solutions. Works with complex datasets using Databricks, SQL, Python, BI tools, and cloud data platforms. Partners with engineering and governance teams, promotes analytics standards, mentors analysts, and communicates insights to stakeholders. Supports strategic decisions through descriptive, predictive, and prescriptive analytics.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Eight-month intern supporting enterprise data, analytics, AI, governance, automation, and digital transformation initiatives. Responsibilities may include building data platforms and pipelines, analyzing data, developing dashboards, supporting AI-enabled solutions, improving data quality, researching emerging technologies, and preparing business materials. Interns collaborate with technical and business stakeholders while gaining hands-on experience with cloud platforms, data engineering, analytics, governance, and AI adoption.

Analyzes usage data across 15+ web applications, builds data pipelines, develops predictive models, and introduces AI/ML tools to improve analytics. Creates executive dashboards and reports, identifies trends and anomalies, communicates actionable recommendations, and ensures data integrity, security, and compliance. Collaborates with technical teams and business stakeholders while documenting analytical methodologies and presenting findings to leadership.

Analyze financial and business data across ARR, retention, billing, subscriptions, and customer cohorts. Build interactive Hex dashboards, translate stakeholder questions into reporting, investigate financial variances, and automate recurring workflows using AI. Partner with Analytics Engineering to improve data models, reporting sources, data quality, and metric definitions. Present insights and collaborate with Finance, Sales, Marketing, RevOps, and Data and Analytics teams.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Develop and enhance Grafana data source plugins integrating third-party services and databases. Support plugin reliability and operations in Grafana Cloud, integrate data sources with agentic workflows, improve engineering processes, assist with customer escalations, and collaborate across the Grafana Division. The role requires strong Golang and TypeScript skills, Kubernetes and microservices experience, robust software development practices, and participation in on-call and SRE operations.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

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.