Top Data Jobs in Calgary

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Labels, categorizes, validates, and quality-checks structured and unstructured data used to train AI and machine learning models. Maintains dataset accuracy, consistency, documentation, and version control while following detailed annotation guidelines. Collaborates with AI engineers, data scientists, and quality teams, identifies errors, provides process feedback, and supports continuous improvement of annotation workflows.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

Own the technical sales motion for NVIDIA-focused enterprise AI infrastructure deals. Lead discovery, architecture, proof-of-concept design and execution, technical closes, partner and field SE enablement, troubleshooting, escalation, customer workshops, and product feedback. The role requires expertise in NVIDIA systems, storage, data platforms, Linux, parallel file environments, GPU clusters, and AI pipelines, with Bay Area availability for on-site partner or customer sessions.

Analyze large datasets to produce dashboards, metrics, and insights supporting product, care, and business decisions. Build BI infrastructure, run A/B and multivariate tests, perform ad-hoc analyses and visualizations, and collaborate with stakeholders and data scientists to inform strategy and operations.

Designs and deploys data integration, data management, reporting functionality, and data models for insurance-sector transformation projects. The role analyzes client needs, configures SAP solutions, applies data modeling expertise, and collaborates with software development and sales teams. Candidates need experience with IT transformation, insurance data processes, SAP FDS or SAP BW solutions, SAP HANA technologies, and technical mapping. The position is Canada-based with 50% travel and occasional on-site work in Calgary or Toronto.

Lead marketing analytics and data strategy for B2B SaaS Marketing. Design measurement frameworks, own attribution, funnel and lifecycle analysis, forecasting, and marketing investment recommendations. Partner with senior marketing leaders and cross-functional teams to build data foundations, enable AI-assisted analytics, and mentor analysts to raise analytical standards.

Provides enterprise-wide architectural leadership for data and AI, defining principles, standards, reference architectures, roadmaps, and governance guardrails. Advises executives and architecture teams on platform decisions, modernization, risk, privacy, security, regulatory alignment, and responsible AI adoption. Oversees major initiatives to improve consistency, reuse, data quality, and long-term sustainability across hybrid legacy and modern environments.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

Lead enterprise data architecture and transformation initiatives for financial-services clients. Responsibilities include defining target-state architectures, operating models, roadmaps, integration patterns, data governance, platform strategies, and architectural standards. The role evaluates vendors and build-versus-buy decisions, leads workshops and delivery teams, communicates with executives, manages complex projects, develops internal intellectual property, and mentors consultants. Strong expertise in MDM, data modeling, integration, cloud data platforms, governance, quality, security, and asset and wealth management data is required.

The GTM Lead for Insurance will build and manage enterprise insurance accounts, develop relationships with senior industry leaders, and shape sales strategies for AI solutions.

Leads and scales a team of Technical Architects serving enterprise customers. Oversees customer onboarding, success, retention, expansion, technical escalations, implementation standards, and customer health metrics. Partners with Sales, Product, Marketing, and Engineering to improve adoption, prioritize customer-driven enhancements, and develop go-to-market strategies. Combines people leadership, technical expertise, coaching, and cross-functional program execution to deliver strong customer outcomes.

Own the vision and roadmap for Wrapbook’s Agreement Data Platform, including agreement data models, rules, APIs, operator tooling, validation, and governance. Partner with engineering and payroll teams on migrations, service boundaries, explainability, compliance, and reliable downstream calculations. Drive discovery through launch, define measurable success metrics, use data to prioritize decisions, and leverage AI tools daily for analysis, coding comprehension, writing, and prototyping.

Coordinate cross-functional programs across HR, Finance, and corporate functions from planning through execution. Manage employee lifecycle workflows, recruiting operations, compliance tracking, finance operations, audits, budgets, reporting, and leadership updates. Identify and implement AI-enabled workflows, translate processes into agent instructions, monitor accuracy and privacy, establish performance metrics, and train internal users. Partner with executives and functional leaders to improve operational processes and adoption of automation.

Lead product design for Waabi’s driverless commercial and operations ecosystem, including fleet management, remote assistance, dispatch, monitoring, and safety-critical workflows. Observe fleet operators and terminal staff, design human-AI interactions, extend the design system, and ship directly in React codebases using AI-assisted tools. Collaborate with product, engineering, research, and operations teams while helping establish design culture and processes as the second product designer.

Design and lead scalable data engineering solutions using Databricks and Azure. Establish enterprise data modeling and engineering standards, architect pipelines and integrations, improve platform reliability and performance, guide modernization and cloud migrations, troubleshoot complex issues, and mentor data engineers while ensuring security, quality, and governance.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

Build highly available, scalable distributed systems and real-time data services supporting Internet-wide scanning, DNS resolution, certificate ingestion, security research, and customer applications. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, frameworks, and cloud or on-premises services; productionizing prototypes; and collaborating with engineering, product, machine learning, and security teams. The role emphasizes maintainable, testable code, reliability, monitoring, and long-term platform development.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Build and improve scalable full-stack products for higher education, owning initiatives across the software development lifecycle from discovery through release. Collaborate with Product, Design, and Engineering in a cross-functional Scrum team, experiment with AI technologies, resolve bugs, complete tickets, and deliver reliable, tested, user-focused features.

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.