Top Data Engineer Jobs in Vancouver

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, workflow automation, evaluation, and observability capabilities. Work hands-on with Python, SQL, dbt, lakehouse data, cloud infrastructure, model selection, and inference-cost optimization. Establish reusable engineering patterns and mentor teams through high-quality code and system design.

Build production AI harness systems for Kinaxis’s Databricks-based enterprise Data Fabric. Responsibilities include developing agent execution frameworks, integrating LLMs with governed data, implementing tool calling and workflow controls, enforcing multi-tenant isolation, and delivering APIs, SDKs, libraries, templates, and operational runbooks. The role requires distributed-systems engineering, cloud data-platform experience, secure multi-tenant design, and practical expertise in agent orchestration, retrieval, evaluation, guardrails, metadata, lineage, and governance.

Lead the architecture and hands-on engineering of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, warehouse design, ingestion, orchestration, DataOps, CI/CD, governance, observability, performance, cost optimization, and AI/ML readiness. Mentor analytical engineers, evaluate emerging tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

Provides hands-on technical leadership for Benevity’s data platform. Designs and builds streaming and batch pipelines, AWS lakehouse and medallion architectures, semantic layers, governed data products, metadata systems, and developer tooling. Establishes data contracts, lineage, quality, observability, and CI-enforced governance while modernizing warehouse environments. Partners with architects and product stakeholders, leads technical strategy, develops proof of concepts, documents architecture, mentors engineers, and applies AI-assisted development practices.

Designs, builds, and maintains enterprise data pipelines, models, governance, security, and quality processes on Azure Databricks and Delta Lake. The role covers ingestion, transformation, medallion architecture, dimensional and normalized modeling, metadata-driven automation, deployment validation, and analytics delivery. Partners with analysts, report developers, stakeholders, and platform administrators to deliver secure, scalable, trusted data solutions across construction and infrastructure businesses.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Build and improve enterprise data products across Match Group brands. Responsibilities include developing reliable ETL pipelines, data models, lakehouse solutions, and scalable data infrastructure; supporting on-premises systems and cloud expansion; translating stakeholder needs into deliverables; defining metrics and data views; maintaining data documentation; training analysts; and advocating for data quality, privacy, performance, and platform improvements.

Build and maintain secure, scalable data pipelines and platform components across AWS and Snowflake. Modernize batch and streaming data flows, support self-serve analytics, and improve testing, observability, security, and CI/CD. Integrate AI capabilities such as anomaly detection and automated tagging. Partner with Finance, Risk, Product, Analytics, ML, and Engineering teams to deliver governed, reliable data while contributing to architecture, design reviews, documentation, and end-to-end project delivery.

Design and maintain scalable backend, full-stack, and AWS data infrastructure supporting millions of daily events and hundreds of integrations. Set technical direction for data pipelines, lead architecture and planning, troubleshoot production issues, collaborate across teams, manage work in Jira, research improvements, and mentor engineers. The role owns initiatives end to end while developing reliable, cost-efficient serverless systems using Node.js, TypeScript, React, and AWS services.

Define the architecture and technical direction of Coalition’s enterprise data platform. Build scalable Snowflake and dbt infrastructure, governed data models, batch and streaming pipelines, reusable data products, feature stores, semantic models, and AI data foundations. Establish standards for data quality, security, lineage, observability, and regulated workflows. Partner across engineering, product, underwriting, actuarial, security, and business teams while mentoring engineers and guiding cross-team architecture.

Build and scale real-time and batch data pipelines, data platforms, and analytics-ready datasets using Kafka, Databricks, Snowflake, Python, and SQL. Design modern data models, ensure data quality and observability, develop AI-powered data experiences, and support business-critical reporting and analytics. Collaborate cross-functionally, improve platform performance, champion engineering best practices, and mentor teammates.

Designs and manages scalable data integration pipelines, governance controls, semantic enrichment, and orchestration systems for enterprise AI and analytics. The role enables LLM, RAG, embedding, and multi-agent workflows; implements security, privacy, compliance, observability, auditability, guardrails, and monitoring for autonomous AI systems. Responsibilities include building connectors and APIs, detecting drift and hallucinations, documenting reusable integration patterns, and collaborating with AI engineers, governance teams, and business stakeholders.

The Software Development Engineer II will design and build backend services and APIs, improve system quality, and support onboarding of new engineers in a distributed systems environment.

Design, build, and deploy scalable, secure data solutions on-premise and in Azure. Collaborate with engineering, security, and ops to implement modern data practices (Databricks, Snowflake, Python, SQL), produce solution designs, support project delivery, and promote DevSecOps and cloud best practices.

Own Dutch’s product analytics and data engineering foundation, including event instrumentation, Segment and Amplitude pipelines, Snowflake and dbt models, orchestration, data quality, governance, and semantic metrics. Partner with product managers on experimentation, build dashboards and data marts, optimize infrastructure and costs, and provide trusted data for AI-powered features. This senior individual contributor role requires strong SQL, Python, analytics engineering, experimentation, and modern data stack expertise.

Leads the architecture, development, reliability, governance, and scalability of batch and real-time data platforms. Designs pipelines, self-service tooling, observability frameworks, and disaster recovery practices; mentors data engineers; establishes engineering standards; and partners cross-functionally to enable trusted data access for analytics, data science, and product teams. The role also contributes to strategic roadmaps, platform innovation, technical debt reduction, and on-call support.

Operate and improve production databases on AWS, including RDS, Aurora, and EC2-hosted systems. Monitor performance, troubleshoot incidents, manage backups and disaster recovery, enforce security controls, and coordinate upgrades and patching. Automate provisioning and routine DBA tasks with Terraform or CloudFormation. The role also maintains documentation, supports schema and query optimization, and collaborates with application, DevOps, and platform teams on reliability and operational excellence.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Build and operate Ground News’s data platform, including warehouse integrations, scalable dimensional models, analytical layers, and KPI infrastructure. Own ingestion pipelines, data quality, monitoring, incident response, infrastructure as code, and deployment workflows across AWS and GCP. Partner with analysts, ML engineers, and product stakeholders to create reliable, reusable data models and ensure accurate, observable, cost-effective data systems.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Lead event-driven and batch processing decisions, establish standards for observability, freshness, and failure handling, and guide cross-functional platform design. Mentor engineers, conduct design reviews, influence teams without direct authority, and drive foundational platform improvements. The role requires deep Python and SQL expertise, cloud data warehouse experience, scalable API-based ETL design, orchestration, and containerized infrastructure experience.

Build and maintain reliable data pipelines, integrations, APIs, and event-driven or batch processing systems. Transform data from advertising, commerce, and order management platforms while improving monitoring, data freshness, performance, security, and scalability. Partner with product, infrastructure, and data engineering teams to expand the integration platform and onboard new sources in a cloud-native, multi-tenant environment.

Own and evolve data infrastructure, distributed pipelines, data modeling, analytics, governance, semantic layers, and customer-facing data products. Operate cloud data systems, support production reliability and on-call incident response, and develop streaming and knowledge-management capabilities. The role also evaluates platform tools, manages stakeholder needs, and contributes to platform design and zero-to-one data system development.

Owns end-to-end data infrastructure, including secure data lakes, streaming applications, data warehouse systems, replication and change data capture pipelines. Responsibilities include architecture, deployment, maintenance, performance optimization, security controls, documentation, operational runbooks, and cross-functional partnership with analytics and product teams. The role requires strategic data infrastructure thinking and production experience in SaaS or data-heavy environments.