Top Data Engineer Jobs in Calgary

Lead the design and development of a data platform, integrating data sources, driving architecture migration, and ensuring robust data governance and API development.

Lead optimization and scaling of Snowflake architecture, build resilient ELT/ETL and MLOps pipelines, implement data observability, automate workflows with AI-assisted tools, and design enterprise semantic models to support analytics and commercial data products.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

Design, build, and operate a cloud-native data platform and ML infrastructure at scale. Own end-to-end pipelines from CDC to feature stores, model training/serving, monitoring, and data product SLAs. Apply distributed systems and MLOps practices, leverage LLMs/agentic systems, and translate technical issues to business impact.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.

Own and operate backend and data services for a cybersecurity platform: design and run high-volume streaming ingestion, ETL/ELT pipelines, lakehouse/warehouse modeling, APIs, and observability. Mentor engineers, contribute across TypeScript/Python/Go microservices on Kubernetes, ensure performance/scale/security, and evaluate AI tools to accelerate development.

Design, build, and maintain scalable cloud data pipelines and architectures. Develop and optimize ETL/ELT workflows, manage data warehouses (primarily BigQuery), implement governance and security, collaborate with engineers and analytics teams, enable ML/AI use cases, and maintain documentation, lineage, and high-availability pipelines.

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.

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

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.

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

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.

Own technical execution of customer data migrations: explore source systems, export data, author declarative version-controlled mappings, transform and validate data, run end-to-end migrations on internal platform, build reusable templates/validation, troubleshoot discrepancies, and document mapping and transformation logic for auditable production cutovers.

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

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

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.

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.

Build scalable ML data pipelines for Waabi's autonomous driving platform. Design, optimize, and manage datasets and training processes while collaborating with scientists and engineers.

Own cloud operational health, architecture intelligence, and cost optimization. Analyze cloud spend, build dashboards/reporting, enforce governance, review architecture for cost impact, and translate technical findings for engineering, finance, and executives.