Top Data Engineer Jobs in Vancouver

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Design, build, and optimize scalable batch and real-time data pipelines and ETL/ELT workflows. Partner with analytics, data science, and business stakeholders to create governed customer data models, identity resolution, and customer 360 capabilities. Enforce data quality, governance, privacy, and security, mentor junior engineers, and improve platform reliability and performance for CDP and customer data products.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Partner with Finance, Engineering, Sales, Product, and Marketing to deliver reliable data for reporting and decision-making. Improve manual workflows, validate data accuracy, and design scalable solutions using modern data modeling practices. Support financial and revenue-related analytics, including ARR and revenue recognition, while balancing immediate fixes with long-term architecture.

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, 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 operate a distributed storage and data movement platform supporting petabyte-scale model training and evaluation. Run stateful systems across Kubernetes clusters, define throughput, latency, and durability requirements with research and infrastructure teams, and solve networking, I/O, consistency, and cross-region data transfer challenges. The role involves supporting cloud object storage, POSIX filesystems, model checkpoints, and large-scale GPU workloads.

Lead Data Engineer responsible for designing, implementing, and maintaining large-scale, reliable data pipelines and cloud infrastructure. Collaborate with product, stakeholders, and cross-functional teams to deliver a SaaS data platform, ensure data quality, apply development best practices, contribute to DevOps and production operations, and support ongoing platform maintenance.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

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.

Own the technical sales motion for NVIDIA-focused enterprise AI infrastructure deals. Lead discovery, architecture, proof-of-concept design and execution, technical closes, partner and field SE enablement, troubleshooting, escalation, customer workshops, and product feedback. The role requires expertise in NVIDIA systems, storage, data platforms, Linux, parallel file environments, GPU clusters, and AI pipelines, with Bay Area availability for on-site partner or customer sessions.

Designs and improves large-scale data processing systems and GCP workflows using Dataflow, BigQuery, Pub/Sub, GKE, and Airflow. Evaluates technologies, establishes engineering standards, leads cross-functional data projects, develops data abstractions with Data Science, mentors engineers, and supports evolving data products.

Design and maintain scalable batch and real-time data pipelines, build governed data models for a CDP, ensure data quality and security, support identity resolution and customer 360, mentor junior engineers, and collaborate with analytics and business stakeholders to deliver actionable customer insights.

Lead the design and evolution of the data platform, including real-time and batch CDC pipelines, Snowflake warehouse architecture, infrastructure as code, reporting infrastructure, data quality monitoring, and scalable data models. Establish standards for incremental processing, late-arriving data, and schema evolution while mentoring engineers and making technical architecture decisions.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

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

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

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

Designs and operates scalable security logging infrastructure, data lakes, ingestion pipelines, and multi-tier storage for enterprise-scale observability. Responsibilities include architecting ELK and OpenSearch environments, optimizing ingestion and queries, implementing infrastructure as code, developing APIs and integrations, managing retention and costs, capacity planning, and supporting disaster recovery. The role partners with security leadership on platform strategy and mentors engineers while enabling analytics and detection engineering across cloud environments.

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.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

Build highly available, scalable distributed systems and real-time data services supporting Internet-wide scanning, DNS resolution, certificate ingestion, security research, and customer applications. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, frameworks, and cloud or on-premises services; productionizing prototypes; and collaborating with engineering, product, machine learning, and security teams. The role emphasizes maintainable, testable code, reliability, monitoring, and long-term platform development.

Build highly available, scalable distributed systems and real-time services supporting Internet-wide scanning, DNS resolution, certificate ingestion, data pipelines, messaging, databases, caching, and APIs. Productionize prototypes, develop reusable tooling and platform capabilities, maintain cloud and on-premise systems, and collaborate with engineering, product, machine learning, and security teams.

Lead and mentor a team of developers, oversee cloud product delivery, drive engineering practices, and ensure high-quality standards while contributing to cross-functional initiatives.