Top Data Engineer Jobs in Calgary

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Provide Tier 4 production support for Snowflake, Databricks, Azure Data Factory, Azure Data Lake and SQL-based data platforms. Triage and resolve incidents, perform root cause analysis, maintain runbooks, improve monitoring/automation, collaborate on releases, and support operational readiness and data quality for scalable, secure, AI-ready data solutions.

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.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Design, build, and scale Snowflake ingestion pipelines and ML/LLM systems. Create agentic tooling to generate, test, and maintain pipelines. Collaborate with analysts, data scientists, and product teams to deliver reliable, high-performance data infrastructure and operationalize models for an AI-first e-commerce platform.

Lead design and operation of Block's real-time, event-driven data systems. Build and run high-throughput streaming pipelines, turn Kafka event streams into low-latency data products, improve reliability and observability, mentor engineers, and define platform architecture and standards for projections, third-party data proxy, and graph data capabilities.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.

Design, build, and operate scalable data ingestion, replication, and lakehouse infrastructure to move petabytes of data into a Delta Lake on S3. Improve reliability, observability, security, and developer experience for Spark/Databricks processing. Develop internal libraries and tooling (Go/Python), collaborate with cross-functional teams, and help shape long-term data platform and AI-ready infrastructure.

Design, build, and operate large-scale Spark/PySpark data pipelines and the enterprise data platform. Implement data integrations, transformations (Python/SQL), monitoring, and data quality. Build MCP servers and AI-enabled tooling, optimize performance, mentor engineers, and lead cross-functional data projects to enable analytics and automation.

Seeking a Data Engineer to design and maintain ETL pipelines, data models, and infrastructure for analytics, working with enterprise clients.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

As an Intermediate Data Engineer, you will develop data infrastructure and products, collaborate with diverse teams, and enhance technical quality across the data team.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Ensure health, stability, and performance of production data platform: respond to incidents, monitor and alert, manage pipelines and APIs, enforce data validation and quality checks, optimize processing performance, collaborate on AWS infrastructure, maintain runbooks/documentation, and support downstream data consumers across multiple business data sources.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Build and operate ML training and serving infrastructure: design, develop, and launch backend systems, support availability and on-call, collaborate cross-functionally, and improve developer velocity.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

The Senior Data Platform Engineer at 1Password will design, build, and maintain data systems and streaming architectures, ensuring data quality and governance for real-time access.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Build and own a versioned, documented gold data layer from lakehouse silver tables for AI research. Transform, validate, and productize multi-modal datasets and pipelines (Databricks/PySpark) to support model development, evaluation, and production ML/LLM workflows while ensuring data quality, provenance, and regulatory controls.