Top Data Engineer Jobs in Vancouver

Designs and maintains reliable data pipelines and computed tables using SparkSQL and PySpark. Integrates IoT, product, customer, external, unstructured, and ML-related data into scalable data models supporting analytics, causal inference, model training, and dashboards. The role requires ETL development, data orchestration, API integration, strong data quality and uptime, and collaboration with Data Science, AI/ML, Analytics, and engineering teams. This is a remote position for candidates residing in Canada.

Designs and builds scalable data pipelines and data lake solutions using Python, Spark, Databricks, SQL, and cloud technologies. Performs data wrangling, feature engineering, validation, monitoring, and logging to support machine learning models and analytics. Partners with data scientists, machine learning engineers, and cloud architects to productionize models, discover datasets, and maintain high-quality data platforms. The role also includes mentoring junior engineers and implementing engineering best practices.

Leads data engineering for AI and machine learning fraud-detection products. Responsibilities include integrating disparate data sources, building scalable and automated pipelines, supporting modeling workflows, deploying production artifacts, creating documented data products, improving reusable processes, collaborating cross-functionally, leading projects, and mentoring junior engineers. Required technologies include Python, PySpark, Airflow, Hadoop, SQL, Databricks, Linux/Bash, CI/CD, and JIRA.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Build and scale reliable Snowflake data pipelines, ML pipelines, monitoring systems, and AI-driven agentic tooling that automates pipeline generation, testing, and maintenance. The role partners with analysts, data scientists, product teams, and analytics engineers to deliver scalable end-to-end data infrastructure for Babylist’s e-commerce platform.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Design, build, and support production data feeds and integration pipelines using Python, SQL Server, Snowflake, REST APIs, SFTP, Docker, and AWS ECS. Develop incremental-loading workflows, apply Data Vault modeling, troubleshoot end-to-end data pipelines, and implement validation, monitoring, logging, error handling, and retries. Collaborate with technical and business stakeholders while maintaining pipeline and operational documentation.

Lead the design and implementation of scalable backend services and APIs for a customer data platform running on AWS. Drive technical design, system reliability, performance, production readiness, and technical debt reduction. Break down complex initiatives, mentor engineers, conduct code and design reviews, and collaborate with stakeholders across teams. Work with distributed systems, relational and non-relational databases, and high-availability production services.

Build and scale data platforms, ETL pipelines, data lakes, warehouses, feature stores, and orchestration tooling. Integrate clickstream, relational, and unstructured data using Databricks and AWS. Partner with data scientists, ML engineers, product managers, and business stakeholders to support analytics, predictive modeling, and data-driven decisions. Maintain production systems, automate testing and monitoring, and communicate technical roadmaps and challenges.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design and build scalable backend systems, APIs, data models, event-driven services, experimentation platforms, and personalization systems. Partner with Product, Data Science, Analytics, and Design teams to support customer acquisition and engagement. Establish secure, observable, high-performance engineering practices, mentor junior engineers, and participate in monitoring, on-call, and operational support.

Own and evolve Later’s data platform across ingestion, orchestration, storage, transformation, and consumption. Build reliable batch and real-time pipelines, backend services, APIs, infrastructure as code, CI/CD automation, observability, data quality controls, and secure GCP-AWS data movement. Modernize BigQuery and lakehouse infrastructure, improve performance and cost, mentor engineers, and establish governance, metadata, lineage, and freshness foundations for analytics, machine learning, LLMs, and AI agents.

Owns the Databricks data lake, dbt models, medallion architecture, ingestion pipelines, data services, schemas, APIs, AI data infrastructure, vector stores, governance, observability, and database optimization. The role requires collaboration across engineering, AI/ML, product, and business teams, along with mentoring engineers and supporting regulated, multi-tenant data systems.

Build reusable Python libraries and reproducible analysis pipelines for image and experimental data; define data models and provenance; partner with wet‑lab scientists to translate requirements and validate methods; establish testing, validation, versioning, and observability for scientific workflows.

Owns biological knowledge representation through LinkML and Biolink ontology engineering, semantic mappings, metadata harmonization, and schema governance. The role administers agentic curation and MCP tooling, develops LLM-driven extraction and validation workflows, and establishes automated quality controls, provenance, CI/CD, and evaluation standards. This senior individual contributor translates biological research requirements into canonical data models supporting genomics, single-cell, screening, imaging, and prior-knowledge datasets.

Own and scale a multi-cloud data platform across AWS and Azure. Manage Apache Airflow, Kubernetes, Terraform, Helm, ArgoCD, CI/CD, observability, big data infrastructure, governance tooling, and FinOps initiatives. Provide end-to-end architectural and operational ownership, improve developer tooling, define platform roadmaps, manage incidents, enforce engineering standards, and mentor data platform engineers.

Own and evolve the Commercial Analytics data platform in Microsoft Fabric. Lead data initiatives from stakeholder requirements through production, architect ingestion and storage solutions, build ELT pipelines and dimensional models, support Power BI reporting, implement validation and monitoring, optimize performance, manage access and governance, document data processes, and establish engineering standards. The role also involves migrating legacy pipelines and reports, evaluating new tools, and mentoring through code reviews and reusable patterns.

Own and architect the Commercial Analytics data platform in Microsoft Fabric. Lead data initiatives from stakeholder requirements through production, designing ELT pipelines, dimensional models, curated datasets, and Power BI integrations. Migrate legacy Azure Data Factory and SSRS workloads, implement validation and monitoring, optimize performance, manage access and governance, document architecture, review code, and evaluate new Fabric capabilities. Collaborate with technical and non-technical stakeholders while establishing engineering standards.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.