Top Data Engineer Jobs in Vancouver

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.

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, build, and operate large-scale security data pipelines, lake/lakehouse storage, and analytics to normalize telemetry from a massive GPU fleet. Ensure data platform security (encryption, RBAC/ABAC, audit), enable trustworthy analytics and detection, collaborate across teams to define data contracts, and maintain data quality, provenance, and freshness for security posture and automated response.

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.

Join a talent network connecting data engineers with VC-backed startups. Build scalable batch and real-time pipelines, data models, warehouses/lakehouses, ensure data quality, support ML pipelines, integrate diverse data sources, and define data architecture and governance.

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.

Design and lead enterprise data architecture for agentic AI, including hybrid lakehouse and graph/vector integrations. Define schemas and knowledge graphs, ensure database performance and governance across Snowflake/Kinetica/BigQuery, establish ETL/ELT and CDC/streaming strategies, and act as technical client lead for architecture, security, and performance benchmarks.

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.

Design, build, and maintain reliable data pipelines and lakehouse assets for batch and streaming workloads. Implement ETL/ELT in Python and SQL, orchestration and monitoring (Airflow/Kubernetes), and retrieval/RAG/vector search pipelines for LLM and AI applications. Ensure data quality, lineage, versioning (Delta/Parquet/Iceberg), cost/performance optimization, and collaborate with MLOps and sports data teams.

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.

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.

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.

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.

Build and own a versioned, documented gold data layer from lakehouse silver tables for AI research. Transform, validate, and productize multi-modal datasets and pipelines (Databricks/PySpark) to support model development, evaluation, and production ML/LLM workflows while ensuring data quality, provenance, and regulatory controls.

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.

Seeking a Data Scientist / ML Engineer to design, build, and maintain scalable machine learning systems, focusing on neural network improvements and ML architecture. Responsibilities include development and deployment of ML models, data pipelines, and MLOps implementation.

Design, build, and maintain production-grade data pipelines and enterprise data models on an Azure Databricks/Delta Lake platform. Implement metadata-driven ingestion, data quality, security (row/column-level), and governance. Partner with stakeholders to deliver scalable analytics, CI/CD deployments, and testing across medallion architecture layers.

The Software Engineer will build and extend the data ingestion pipeline, find audio data sources, and work with scientists to enhance datasets for AI models.

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.

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

Lead architecture and long-term direction of a multi-petabyte data platform powering AI-driven advertising. Drive cross-team initiatives across data engineering, ML, and product; improve scalability, reliability, cost-efficiency, and operational excellence; mentor senior engineers and influence platform strategy and modernization.

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.

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.