Top Data Engineer Jobs in Seattle, WA

Design, develop, and maintain end-to-end data pipelines and data products for analytics. Troubleshoot complex data issues, document data sources, onboard backend and 3rd-party data, mentor peers, evaluate and deploy new tools, and collaborate with business partners to enable data-driven decisions.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Design and build foundational dimensional data models, enterprise metrics, and curated data products on a modern lakehouse. Partner with business domains to translate processes into scalable, high-quality datasets, optimize analytical workloads, and operate distributed processing systems to support analytics, BI, and AI.

Lead architecture and implementation of a unified, multi-tenant Data Lakehouse on AWS and Snowflake. Design high-throughput ingestion, medallion ELT pipelines, semantic layers, RBAC/security, and IaC. Mentor staff engineers, drive standards (dbt, PySpark, Snowpark, Terraform), and partner cross-organization to enable self-serve data access for analytics, ML, and AI workloads at petabyte scale.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Operate and evolve AMP platform to ensure reliability, security, compliance, cost efficiency, and operational excellence. Monitor health and capacity, manage incidents and escalations, provision Azure/Fabric resources, enable onboarding, drive security and compliance remediation, optimize costs and capacity, coordinate with product groups and vendors, and improve automation, observability, and operational tooling.

Own and evolve the data lake and analytics stack powering fleet observability. Build and maintain scalable ETL/ELT pipelines, operate data lake infrastructure (Iceberg, Trino, Airflow, Spark, Superset), produce analytics and executive reporting, create dashboards and runbooks, and implement data governance and observability.

Design, develop, and optimize Teradata data solutions: elicit requirements, create logical/physical designs, write SQL (views/procs/macros), use Teradata utilities for data loads, tune performance for large datasets, coordinate stakeholders, and write Unix shell scripts.

Elicit and analyze requirements; design logical/physical Teradata solutions; develop ETL using Teradata utilities; write SQL (views, stored procedures, macros, functions); perform performance tuning for large datasets; write Unix shell scripts; coordinate with stakeholders and support BI/DW initiatives.

Build and maintain data infrastructure to measure agent behavior and user interactions, design evaluation frameworks for AI agents, analyze user behavior to inform product strategy, and partner with engineering and leadership to solve high-impact business problems using data.

Design, build, and operate scalable cloud data pipelines and ELT/ETL processes (batch and streaming) into Snowflake. Automate monitoring, triage incidents, optimize performance and costs, enforce access controls, and provide on-call support. Collaborate with Product, Platform, and Architecture teams, document runbooks, and mentor junior engineers while researching emerging tech and improving observability and CI/CD for data deployments.

Design and build scalable ETL/ELT pipelines, data marts, semantic and feature layers, and model-ready datasets. Partner with data scientists and analysts to translate analytical needs into performant SQL and productionized pipelines, ensure data quality/lineage, optimize performance, and mentor teammates while participating in on-call support for production data services.

Lead architecture and evolution of Headway's data platform (warehouse, ingestion, orchestration, CI/CD, monitoring, cloud infra). Serve as technical anchor across analytics, product, and ML teams, drive platform roadmaps, set standards, mentor engineers, and own end-to-end infrastructure decisions for scale and performance.

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.

Lead architecture and delivery of scalable data infrastructure: design and optimize ETL/ELT pipelines, enterprise data models, and data quality frameworks. Define platform standards, mentor engineers, drive cross-functional initiatives, and partner with analytics, data science, and engineering stakeholders to ensure reliable, production-grade data assets.

Design, build, and maintain complex batch and streaming data pipelines and integrations. Collaborate with engineering and analytics teams to model data, implement data exchange systems, automate and optimize processes, support dev/test/prod environments, ensure data quality, and participate in on-call rotation.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead design and implementation of enterprise data platforms, real-time pipelines, feature stores, and agentic data infrastructure. Ensure data governance, quality, lineage, and reliability while partnering with scientists, engineers, and product leaders. Own technical roadmap, mentor engineers, and drive platform scalability and engineering best practices.

Build and maintain ETL/ELT pipelines using Databricks, DBT, and Snowflake to ingest data from Salesforce, Dynamics 365, Coupa, Workday, and Concur. Create reusable data models and deliver curated datasets for AI/ML, implement DBT tests, documentation, lineage, and ensure performance, cost efficiency, and governance compliance while collaborating with data science teams.

The Principal Data Engineer will lead development projects, design scalable data pipelines, ensure data quality, and mentor a team of engineers while collaborating with stakeholders.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.