Top Data Engineer Jobs in Vancouver

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.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.

Design, develop, test, and deploy high-performance Apache Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and platform performance, build data pipelines using HDFS, Hive, Impala, HBase, and Kafka, ensure data integrity and security, collaborate with data scientists and analysts, and implement version control and CI/CD for deployments.

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.

As a Senior Data Engineer, you will build scalable data pipelines, manage cloud infrastructure, and support analytics and data science teams, leveraging various technologies to deliver impactful solutions for clients.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from ad platforms, order systems, and real-time events. Ensure reliability, observability, and maintainability across event-driven and batch architectures, implement authentication flows, optimize data workflows in a cloud-native environment, and collaborate with product, infrastructure, and data teams to onboard new sources.

The Data Visualization Developer will create and support dashboards in Power BI, develop SQL scripts, perform regression testing, and troubleshoot existing scripts.

The Staff Backend Engineer will architect high-throughput streaming systems, define technical direction, mentor engineers, and solve complex problems for Helius' core products.

Design and optimize SQL Server solutions, manage data, automate processes with PowerShell, and integrate with external systems to enhance functionality.

As a Backend Engineer on the Data team, you will design and maintain data pipelines and services, ensuring data quality and scalability while collaborating with cross-functional teams to meet data needs.

Own analytics enablement by translating stakeholder questions into trusted, reusable metrics and semantic models. Build and maintain the metrics/semantic layer, validation, documentation, AI-accessible datasets, and pipelines to ingest new sources. Enable self-serve analytics and agentic LLM querying (Claude) while partnering with product, GTM, and executives.

Design and maintain scalable data pipelines, manage Kubernetes environments, ensure operational excellence and security in data infrastructure solutions.

Design, deploy, and operate large-scale, highly-available data pipelines and ML data workflows for anti-cheat telemetry. Build GitOps deployment workflows, observability (metrics, logs, alerts), ensure data integrity, support batch/online model deployments, own incident response and operational excellence.

Seeking a Senior Data Engineer to develop and maintain data pipelines using Microsoft Fabric and Azure Data Factory, ensuring data security and compliance while optimizing data solutions and collaborating cross-functionally.

Build and maintain scalable, reliable data systems and pipelines, uphold SLOs, partner with Product/Sales/Finance, enable self-service analytics, contribute to data platform strategy, and improve engineering practices for testing, reliability, and documentation.

Design, develop, and optimize scalable data solutions and data pipelines using Databricks, Python, SQL, and Azure. Collaborate across teams for data integration and quality assurance.

Responsible for designing, building, and maintaining scalable data pipelines using Databricks. Optimize ETL/ELT processes and ensure data quality, governance, and security.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

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

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Design and maintain data systems for business-critical insights, connect data across platforms, build ETLs, and support business data decisions.

The Senior Data Engineer is responsible for architecting and delivering scalable data infrastructure and pipelines while collaborating across teams, integrating AI capabilities, and mentoring other engineers.