Top Data Engineer Jobs in Calgary

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.

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.

Design, build, and operate production AI systems and marketing data infrastructure. Develop retrieval pipelines, agent orchestration, CDP integrations, ETL/ELT pipelines, and analytics. Automate workflows with AI, ensure data quality and reliability, partner with stakeholders, ship Python and SQL code, mentor engineers, and drive requirements to production.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Lead technical implementation of a market management platform for nodal electricity markets. Design and operate large-scale, data-intensive production systems handling continuously changing data. Make architecture decisions, contribute production software, manage incidents, improve existing systems, and partner with product, engineering, data science, and power-market experts. Lead technical reviews, mentor engineers, and drive optimization, forecasting, and analytical capabilities.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

Design and operate scalable, cloud-native data pipelines and orchestration systems for imaging, biomarker, and healthcare data. Build Databricks and distributed processing workflows, integrate DICOM data into data lakes, and implement validation, observability, monitoring, and fault tolerance. Collaborate with data science, AI, R&D, product, and data management teams to deliver secure, compliant, reliable data for analytics and machine learning.

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.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

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.

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.

Lead end-to-end data and ML pipelines for onboarding and fraud decisions: ingest streaming and batch sources, build feature platform and entity resolution, productionize ML models, enforce PII/data governance, and set technical direction while mentoring engineers and ensuring low-latency, reliable serving.

The Guidewire Data Engineer will design schemas in Snowflake, develop ETL processes, optimize SQL queries, and integrate Guidewire with data platforms.

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

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.

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 scale real-time and batch data infrastructure and pipelines (Kafka, Databricks, Snowflake) to power analytics, reporting, and AI-powered data experiences. Drive data modeling, governance, observability, and best practices while partnering with cross-functional teams and mentoring engineers.

Designs and leads scalable data architectures across cloud platforms, including data lakes, warehouses, analytics platforms, ETL/ELT pipelines, streaming ingestion, and data governance. Provides technical leadership, mentors engineers, optimizes performance and reliability, supports data migrations to Databricks Lakehouse, and advises clients on modernization and architecture decisions. Collaborates with stakeholders and analytics teams to deliver solutions for reporting, analytics, and AI initiatives.

Designs, builds, and maintains scalable batch and real-time data pipelines supporting an IoT safety ecosystem. The role manages ETL/ELT processes, data lakes, warehouses, lakehouses, orchestration, monitoring, data quality, and observability. It partners with product, engineering, analytics, and data science teams to deliver reliable data services, productionize models, and shape long-term data platform architecture using AWS and modern infrastructure practices.

Design, build, and optimize Microsoft Fabric-based ingestion, transformation, and curated data layers. Migrate legacy sources, implement business rules, develop API-driven workflows, ensure data quality, security, and monitoring, and support CI/CD and operational delivery for analytics and reporting.