Top Data Engineer Jobs in Vancouver

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, 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.

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

Lead design and implementation of Jane's customer data infrastructure: own event collection, pipelines, CDP tooling, schema governance, identity resolution, and attribution. Work cross-functionally with marketing, BI, product, and engineering to set standards, evaluate tooling (e.g., RudderStack), ensure privacy-compliant instrumentation, and build reliable, consistent data delivery to the warehouse.

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.

Lead design and implementation of scalable, secure Forma Data cloud services. Act as technical lead across full software lifecycle, write and operate cloud services, mentor engineers, solve complex distributed-system problems, and collaborate globally to integrate SaaS products with Autodesk tools.

Design, build and maintain automated, self-serve dashboards and analytical interfaces. Create interactive visualizations, ensure performance and scalability, drive adoption, lead product lifecycle for Data & Analytics products, mentor teammates, and collaborate with cross-functional stakeholders to enable data-driven decisions.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

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.

Own and evolve high-throughput backend services that deliver machine learning-powered predictions to merchants. Responsibilities include API design, service decomposition, Python development, PostgreSQL and Kafka integration, distributed systems reliability, observability, feature pipelines, ML serving, model deployment, versioning, rollback, and prediction monitoring. The engineer will partner with Data Science, lead architectural decisions and technical reviews, mentor teammates, and establish strong engineering practices for production data products.

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

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

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

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.

The Senior Data Platform Engineer will design and operate a unified data platform, ensuring reliable data flows and collaborating across teams to shape data strategy and architecture.

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.

Lead design and implementation of CDC-based real-time and batch data pipelines (Debezium, Kafka, Flink), own Snowflake strategy and medallion architectures, build infrastructure-as-code with Pulumi, maintain BI/reporting via Sigma, define data quality/monitoring, translate business requirements into scalable data models, and mentor engineers.

Own the design, development, and operation of scalable AWS data infrastructure, including pipelines, ETL processes, data warehouses, data marts, and data quality systems. Build infrastructure with Terraform, optimize reliability and cost, support analytics and financial reporting, ensure security and compliance, troubleshoot complex issues, and mentor junior engineers. Collaborate with finance, product, analytics, and engineering teams in a remote fintech environment.

Own and drive the architecture and roadmap of ClickUp's data platform. Build scalable, reliable data pipelines and AI/ML infrastructure using AWS serverless, Snowflake, dbt, and Terraform. Lead cross-team technical initiatives, optimize cloud costs, establish engineering standards (observability, testing, CI/CD), mentor senior engineers, and influence org-wide architecture decisions.

Establish a single source of truth for customer data by reconciling backend and CRM records, building a two-way HubSpot-backend integration, owning reporting metrics, running onboarding data-mapping sessions with labs, and delivering engineering briefs to remove mapping friction.

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.

Design, build, and maintain scalable data pipelines, optimize Databricks/Spark and Delta Lake architectures, deliver trusted datasets for analytics and ML, implement observability and data quality, and drive platform architecture and team mentorship.

Owner of Float's Snowflake architecture and ingestion layer: build orchestration, observability, data quality and contracts, CI/CD for data code, support ML pipelines, and collaborate with analytics, data science and product teams to deliver reliable, cost-efficient, governed data.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Design, build, and optimize cloud-native lakehouse pipelines and curated data products using Python and SQL. Own data modeling, orchestration, observability, performance tuning, governance, and secure access for multi-tenant SaaS analytics and AI use cases. Mentor engineers, lead reviews, troubleshoot production issues, and drive reusable platform patterns to support reporting, embedded analytics, operational data products, and ML-ready datasets.