Top Data Jobs in Calgary

Embedded on a central Data Science team, you will define problems, build metrics/reports, run analyses and experiments, surface insights, and drive decisions end-to-end for growth, product, AI/automation, or partnerships.

Embedded on a business unit, define analytic questions, build metrics/reports, run analyses and experiments, surface insights, and drive data-driven product, growth, AI, or partnership decisions end-to-end.

Lead architecture and delivery of production-grade machine learning systems, mentor global ML engineers, drive model lifecycle (training, deployment, monitoring, retraining), and apply advanced ML (fraud detection, recommendation, churn/LTV) with MLOps and inference optimization.

Build, maintain, and troubleshoot ELT/data pipelines from multiple structured and unstructured sources. Support analysts by developing requirements and solutions, implement web-scraping for public data, and monitor production ELT processes and incident response.

Design, build, and maintain ETL/ELT pipelines and the Bronze/Silver layers of a Databricks Delta Lake lakehouse. Ingest batch and near-real-time data, implement data-quality checks, monitoring, lineage, governance, and secure access. Troubleshoot pipeline failures, design scalable schemas, and document architecture, integrations, and dependencies.

Provide strategic legal counsel for the Global High-Tech Data Center business: draft and negotiate complex commercial agreements, advise senior leaders on legal risk, design contracting processes, manage outside counsel, coordinate cross-functional legal matters, and lead talent development within the legal team.

Evaluate and rank multimodal model outputs, stress-test and document failures, author and edit dataset prompts/responses, build and apply rubrics, perform high-quality annotations across text, image, and structured formats, participate in calibration checks, adapt to evolving tasks, and report model performance trends to cross-functional teams.

The Software Engineer will provide technical support to customers, collaborate with engineering teams, and drive customer success initiatives while leveraging Python and SQL expertise.

Contract Data Analyst to design, build, and maintain Tableau dashboards, prepare and clean raw data in Databricks/data lakes, partner with stakeholders, support testing/validation/documentation, and ensure governed, scalable analytics across enterprise domains.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

As a Software Engineer, you will create tools and pipelines for AI development, manage data solutions for ML teams, and ensure high-quality code.

The Data Platform Marketing Manager will lead marketing strategies for DoiT's data lake and Snowflake optimization business, aiming to drive growth and impact.

Build and operate a reliable data platform: monitor infrastructure and pipelines, automate platform tasks, manage IaC and CI/CD, support RBAC and governance, contribute to data pipelines and migration, document runbooks, and participate in on-call incident response while improving AI-assisted engineering workflows.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead design, development, and deployment of GenAI and NLP solutions (RAG, LLM-powered apps, agentic AI, Document Intelligence). Work with clients to translate business needs, build end-to-end ML pipelines, mentor teams, present results to stakeholders, and operationalize scalable, governed AI systems.

Own and maintain procurement master data in the ERP, partner with stakeholders and suppliers to gather requirements, analyze and correct data, build reports/dashboards (PowerBI), support system enhancements and governance, and lead continuous improvement and mentorship for procurement data processes.

Serve as the technical bridge for GTFS: drive specification change requests, support implementers, maintain documentation, facilitate community discussions, represent MobilityData at events, and provide technical guidance to internal teams to improve data quality and adoption.

Lead the Growth pod as the first dedicated data hire: build data infrastructure connecting event, product, GTM, and financial data; define metrics and run experiments with proper power analysis; build predictive models and reporting/automation; proactively surface insights and embed with Product, Engineering, and Design to drive activation, expansion, and retention. Bring applied AI to improve growth outcomes.

Design and implement data models, pipelines, and ML systems to power AI agents and analytics. Build, deploy, and monitor machine learning models at scale, integrate Snowflake/AWS and Salesforce Data Cloud, and deliver production-ready data products and views to stakeholders.

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

As a Software Engineer in Data Infrastructure, you will build and maintain high-performance data storage systems for AI training, collaborate with experts, and optimize data processing.

Design business data models and data architectures, mentor data teams, translate business requirements into technical specifications, and set standards for AI-augmented development practices.