Top Data Engineer Jobs in Vancouver

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

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.

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.

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.

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.

Build and operate backend services for vehicle telemetry and control: design and deliver features in Go/Python/Rust, manage high-scale analytical stores and migrations, implement stream processing and tiered storage, ensure observability and production ownership, and collaborate across teams to support fleet-scale vehicle data systems.

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.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Perform L3 network troubleshooting and RCA on production enterprise networks, review telemetry/logs/configs, label and annotate network behaviors for ML training, and help define schemas and data pipelines to support AI-driven infrastructure.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design and implement automated data quality frameworks and tests for BigQuery-based pipelines. Collaborate with data engineers and product to define data contracts and quality gates, build observability and anomaly detection, validate complex ELT/ETL processes, and integrate data tests into CI/CD workflows. Drive improvements in documentation, governance, and pipeline reliability.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Design, build, and optimize cloud-native lakehouse pipelines and curated data products using Python and SQL. Own data modeling, performance tuning, observability, governance, and orchestration. Enable multi-tenant SaaS reporting, analytics, and AI-ready datasets; troubleshoot production issues; mentor engineers and drive platform architecture and reusable engineering patterns.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.

Design, build, and scale cloud lakehouse data products (Databricks/Snowflake) from ingestion to analytics-ready datasets using medallion architecture. Develop reusable Python frameworks, Spark pipelines, and dbt/SQL transformations. Implement data modeling, governance, observability, validation, and SLA monitoring. Troubleshoot production issues, adopt AI to accelerate engineering, and provide technical leadership, code reviews, architecture decisions, and mentoring for global engineering teams.

Own and operate backend and data services for a cybersecurity platform: design and run high-volume streaming ingestion, ETL/ELT pipelines, lakehouse/warehouse modeling, APIs, and observability. Mentor engineers, contribute across TypeScript/Python/Go microservices on Kubernetes, ensure performance/scale/security, and evaluate AI tools to accelerate development.

Lead insider risk and data loss investigations from triage to closure, operate and tune DLP across environments, build detections and workflows, partner with HR/Legal/Compliance/IT, mature insider risk program, support audits, and mentor others.

Own and improve annotation systems, workflows, and tooling to produce high-quality training data. Build Python and web tools, integrate model-assisted labeling and active learning, implement data quality checks, support dataset versioning and evaluation, and coordinate cross-team efforts to measure and improve annotation throughput and model impact.

Design and build operational data systems, including pipelines, storage layers, and APIs for near real-time enforcement and insights, balancing streaming and batch paradigms.

The Senior Data Engineer will lead the design and development of scalable data infrastructure for analytics and AI capabilities, ensuring compliant data processing systems and improving the data platform.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

You will architect and develop new features for SELECT, focusing on cost management, UI components, data visualization, and improving application performance.

Own technical execution of customer data migrations: explore source systems, export data, author declarative version-controlled mappings, transform and validate data, run end-to-end migrations on internal platform, build reusable templates/validation, troubleshoot discrepancies, and document mapping and transformation logic for auditable production cutovers.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

As a Software Engineer, you will create tools and pipelines for AI development, manage data solutions for ML teams, and ensure high-quality code.