Top Data Engineer Jobs in Vancouver

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.

Build and improve scalable full-stack products for higher education, owning initiatives across the software development lifecycle from discovery through release. Collaborate with Product, Design, and Engineering in a cross-functional Scrum team, experiment with AI technologies, resolve bugs, complete tickets, and deliver reliable, tested, user-focused features.

Own Jetson’s marketing data lake end to end, including source integrations, dbt medallion models, data quality, attribution, experimentation infrastructure, LTV and ROI models, Metabase dashboards, and analytics for marketing and growth teams. The role combines hands-on data engineering with measurement and strategic analysis across paid, organic, direct mail, CTV, partnerships, and offline channels.

Own and operate a cloud data platform, including ingestion, transformation, orchestration, serving, governance, lineage, and data quality. Build automated reporting, AI-assisted workflows, feature pipelines, and predictive-use-case infrastructure. Develop production-quality code, CI/CD, testing, observability, access controls, PII classification, versioning, and MLOps controls. Collaborate with engineering, product, finance, security, and other stakeholders while managing system cost, latency, reliability, and model risks.

Operate and support production databases on AWS, including RDS, Aurora, and EC2-hosted systems. Monitor performance, troubleshoot incidents, optimize capacity, manage security, backups, recovery, replication, upgrades, and patching. Automate DBA processes using Terraform or CloudFormation, maintain documentation and runbooks, and collaborate with application, DevOps, and platform teams on reliability, releases, and incident reviews.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Lead architecture and performance improvements for data-intensive distributed systems. Optimize query plans, indexing, caching, and latency (P95/P99) while contributing to JVM backend services and ensuring scalability and reliability for customer-facing platforms.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

The Data Engineer will assist clients with SAP security design, implement SAP security, develop authorization profiles, and conduct security reviews.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Lead the migration of Vanta’s compliance data platform from MongoDB to schema-aware PostgreSQL while maintaining backward compatibility and data integrity. Design high-throughput Kafka and S3 pipelines, eventual-consistency safeguards, query APIs, tenant isolation, caching, and distributed storage systems. Set architectural direction, mentor engineers, drive cross-team technical roadmaps, and improve reliability, observability, and operational practices for auditable enterprise-scale infrastructure.

Lead the architecture and development of Blackpoint Cyber’s enterprise data platform. Own data ingestion, storage, transformation, governance, observability, lifecycle management, and cost optimization. Design scalable pipelines and medallion architecture for cybersecurity events, survey data, business intelligence, analytics, reporting, and AI. Drive multi-cloud integrations, data quality, reliability, compliance, and platform roadmap decisions while mentoring senior data engineers and collaborating across Product, Engineering, AI, and Security teams.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

Designs and develops enterprise cloud data solutions, including batch, near-real-time, and API-based pipelines. Builds data models, warehouses, lakes, and lakehouses while implementing governance, quality, lineage, and curation practices. Supports CI/CD, testing, monitoring, observability, security, and compliance. Collaborates with architects, product owners, analysts, and leadership on platform strategy and modernization. Mentors engineers, leads reviews, presents technical topics, and explores AI-enabled data capabilities.

Lead the event instrumentation and analytics stack: own Segment-to-Amplitude pipelines, build and maintain dbt models in Snowflake, manage orchestration with Prefect, design and analyze experiments, implement data quality and governance, and deliver semantic-layer metrics and data marts for BI and AI features.

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.

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.

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.

The Lead Engineer for BI will design data solutions, lead project teams, mentor, and drive BI strategy aligning with business needs.

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.