Top Data Engineer Jobs in Calgary

Build a functional prototype addressing a real RBC business challenge within a collaborative summer innovation program. Responsibilities include data transformation, engineering, cleaning, mining, product design, troubleshooting, development, stakeholder collaboration, research, strategy, communications, and executive presentations. Participants work with mentors and subject matter experts while applying data science, programming, agile development, and design-thinking techniques.

Design, develop, and maintain scalable data pipelines and architectures for analytics and reporting. Perform ETL and streaming data integration, data modeling, governance, quality management, and workflow optimization across cloud and big data environments. Translate business requirements into data solutions, monitor reliability and performance, and collaborate with stakeholders to advance Walmart’s data strategy.

Designs and maintains reliable data pipelines and computed tables using SparkSQL and PySpark. Integrates IoT, product, customer, external, unstructured, and ML-related data into scalable data models supporting analytics, causal inference, model training, and dashboards. The role requires ETL development, data orchestration, API integration, strong data quality and uptime, and collaboration with Data Science, AI/ML, Analytics, and engineering teams. This is a remote position for candidates residing in Canada.

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.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Own and deliver highly available, low-latency backend identity systems supporting consumer onboarding, authentication, and verification. Lead quarterly team goals, guide engineers through complex problems, collaborate with product and design stakeholders, improve technical and code quality standards, monitor system availability, support on-call operations, and mentor team members.

Designs and demonstrates secure, scalable Azure database and analytics solutions for enterprise customers. Leads technical sales engagements, proofs of concept, hackathons, architecture workshops, migrations, and production deployments. Advises platform leaders, resolves technical blockers, collaborates with engineering, and develops expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, on-premises data warehouses, Hadoop, and competing analytics platforms.

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.

Designs, builds, and optimizes production-grade data pipelines using Azure, Databricks, Delta Lake, and Apache Spark. Develops ETL and data integration solutions, automates ingestion and validation, and ensures pipeline reliability, security, and compliance. Leads client data engineering engagements, translates business requirements into scalable architectures, collaborates with stakeholders, mentors junior engineers, and supports analytics and machine learning initiatives.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Design, develop, and support business applications, automations, and AI-enabled solutions. Build full-stack applications, prototypes, and production systems using modern cloud, DevOps, and generative AI technologies. Collaborate with stakeholders, architects, data teams, and infrastructure teams to deliver secure, reliable solutions. Support applications throughout their lifecycle, contribute to engineering standards, troubleshoot production issues, and mentor developers within Agile delivery processes.

Leads large-scale software engineering projects from design through deployment, provides technical leadership and mentorship, contributes to architecture and engineering standards, drives testing and software quality, resolves production issues, develops scalable solutions, and collaborates across Product, UX, and Engineering teams. The role also supports Agile delivery, incident reviews, prioritization, stakeholder communication, and cross-team technical alignment.

Build and operate scalable analytics data platforms using Java, Python, Spark, Airflow, Iceberg, and AWS. Responsibilities include developing batch pipelines, automated testing frameworks, CI/CD validation, data quality controls, representative test datasets, and release gates. The role also advances AI-driven engineering workflows, supports production reliability through on-call participation and incident analysis, and collaborates with distributed engineering teams to improve platform quality, scalability, and observability.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Own data engineering for workforce forecasting and staffing products. Build and operate Databricks pipelines, lead the Redshift-to-Databricks migration, manage database replication and file ingestion, establish data contracts, handle PII and data residency, monitor quality and costs, and support on-call operations. The role also requires documentation, infrastructure-as-code, collaboration with product and analytics teams, and pipelines supporting forecasting or machine-learning models.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Design, develop, and own data pipelines, models, data marts, and data products supporting Product, Data Science, and GTM teams. Build data infrastructure and services using Spark, Scala, Java, SQL, Python, and Airflow; maintain pipeline and application SLAs; investigate data quality issues; leverage LLMs and agents; and collaborate cross-functionally to architect shared datasets and business reporting solutions.

Build infrastructure for reproducible robot training and evaluation datasets, including multimodal ingestion, temporal alignment, provenance, quality controls, storage, versioning, replay, and reliable delivery. Own schemas, lineage, observability, failure recovery, safe backfills, and dataset reproducibility across video, sensor, command, intervention, outcome, and machine-health data.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Design, build, and support production data feeds and integration pipelines using Python, SQL Server, Snowflake, REST APIs, SFTP, Docker, and AWS ECS. Develop incremental-loading workflows, apply Data Vault modeling, troubleshoot end-to-end data pipelines, and implement validation, monitoring, logging, error handling, and retries. Collaborate with technical and business stakeholders while maintaining pipeline and operational documentation.

Build data and evaluation systems for Glean’s AI products, including benchmark datasets, scalable eval infrastructure, agent observability, LLM-based verifiers, CI and production evaluation pipelines, and enterprise simulation environments. Analyze traces and failures, measure quality with statistical rigor, and translate findings into product improvements. Partner with research, engineering, product, and go-to-market teams to improve agent performance while balancing quality, latency, and cost.