Top Data Jobs in Calgary

Lead product analytics by building scalable data models and measurement frameworks, owning dbt workflows, performing cohort/retention and funnel analyses, defining KPIs, automating analytics, and presenting recommendations to influence product and business decisions.

Own and maintain core financial datasets and dbt models; write complex SQL; build and optimize Looker dashboards; troubleshoot data quality; and deliver reconciled, actionable financial insights to Finance and Payments stakeholders.

Own and improve annotation systems, workflows, and tooling to produce high-quality training data. Build Python and web tools, integrate model-assisted labeling and active learning, implement data quality checks, support dataset versioning and evaluation, and coordinate cross-team efforts to measure and improve annotation throughput and model impact.

Design and execute data quality strategies for enterprise data platforms. Test ETL/data pipelines, perform source-to-target reconciliations, build validation frameworks, write complex SQL for analysis, perform regression testing, root-cause defects, and collaborate with engineers and stakeholders to improve data integrity.

Manage end-to-end LiDAR deliverable production, coordinating acquisition, processing, QA, and client communication to deliver LAS/LAZ point clouds, DEMs/DSMs, intensity rasters, and related products on schedule and to specification.

Design and optimize data pipelines, lead machine learning feature integration, mentor junior engineers, and enforce data governance.

Design and build operational data systems, including pipelines, storage layers, and APIs for near real-time enforcement and insights, balancing streaming and batch paradigms.

Design, build, and deploy ML models across diverse use cases; work with managed cloud ML platforms and containerized solutions; collaborate with engineers on production integration and MLOps best practices; preprocess and analyze large datasets; mentor teammates and influence technical direction; translate complex ML concepts for technical and non-technical stakeholders.

Lead end-to-end client ML engagements: scope problems, design, build, and deploy production ML solutions across cloud and containerized stacks. Lead and mentor small teams, manage client relationships, evaluate emerging tools, and ensure models are production-ready and explainable to technical and non-technical stakeholders.

The Senior Data Engineer will lead the design and development of scalable data infrastructure for analytics and AI capabilities, ensuring compliant data processing systems and improving the data platform.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

As a Lead Data Scientist, you'll design experiments, build predictive models, and lead a team while shaping data-driven strategies to enhance AI deployments.

Lead and support analytics lifecycle activities: manage analytic data platforms, govern data access, design and implement reporting/BI, apply advanced quantitative modeling, deliver actionable business insights, contribute to policies, work independently on complex projects and mentor junior colleagues.

Manage construction and maintenance of de-identified, FISMA-compliant research datasets; implement secure storage, encrypted transfer, and data retention/destruction. Coordinate with qualitative and regulatory teams, support incident response, and deliver formatted codebooks and CDC-requested dataset formats.

Join a SignalFire talent network to be matched with VC-backed startups seeking Senior/Staff Data Scientists. Responsibilities include experimental design and analysis, predictive and causal modeling, productionizing models, building metrics and dashboards, advising product and business teams, mentoring other data scientists, and shaping data strategy and tooling at fast-moving startups.

Join a talent network connecting data engineers with VC-backed startups. Build scalable batch and real-time pipelines, data models, warehouses/lakehouses, ensure data quality, support ML pipelines, integrate diverse data sources, and define data architecture and governance.

You will architect and develop new features for SELECT, focusing on cost management, UI components, data visualization, and improving application performance.

Design and build audit-ready data architecture and ETL pipelines, maintain inventories/playbooks/catalogs for audit, implement automated validation and monitoring, assess change impacts, and translate audit requirements into technical specifications for stakeholders.

Lead program management and coordination between the Data team and Audit, Risk, Security, Finance, Compliance, and external auditors. Own continuous audit readiness by building and maintaining inventories, playbooks, catalogs, tagging and governance processes, and auditor access controls. Translate audit engagements into repeatable, documented responses and run mock testing to identify improvements and reengineering needs.

Design and maintain production-grade data models and pipelines across the full stack (ingestion to visualization). Build scalable dbt models, improve manual workflows, ensure data accuracy and validation, and collaborate with Finance, Engineering, Sales, and other teams to deliver trusted analytics and reporting.

Lead the data solutions strategy for PR&C PSP division, managing data initiatives, teams, and collaborating on analytics projects. Ensure compliance, governance, and optimize data services while communicating technical solutions to stakeholders.

Partner with Marketing, Sales, Finance, Product and other teams to turn business questions into actionable analysis. Own end-to-end analytics: build datasets and dashboards (dbt, LookML), maintain metric definitions and caveats, use and curate agent tooling for trustworthy answers, and contribute small data platform and pipeline improvements.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

The Senior Data Platform Engineer will design and operate a unified data platform, ensuring reliable data flows and collaborating across teams to shape data strategy and architecture.

Remote Data Researcher responsible for conducting data research, designing and implementing surveys, performing data entry, analyzing datasets to identify trends, preparing reports and presentations, and collaborating with cross-functional teams to provide actionable recommendations.