Top Data Engineer Jobs in Seattle, WA

Build, maintain, and optimize complex batch and streaming data pipelines across structured and unstructured sources. Partner with Engineering and Analytics teams to develop data exchange systems, translate business needs into technical solutions, ensure data and code quality, automate processes, support development environments, and participate in weekly on-call rotations.

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.

Build and operate production data pipelines and dimensional models using Spark, SparkSQL, and cloud lakehouse technologies. Own pipelines from requirements through deployment, monitoring, and iteration; improve data quality, lineage, reliability, and cost efficiency. Partner with data scientists, analysts, product managers, and engineers to support datamarts, KPIs, reporting, and analysis. Participate in business-hours on-call rotations and improve runbooks and alerting.

Design, build, and operate large-scale ETL pipelines, data lakes, and data platforms processing billions of daily events. Own data quality, freshness, reliability, monitoring, and observability for foundational datasets. Develop self-service ETL frameworks and tooling, contribute to architecture and technical strategy, and partner with product, engineering, machine learning, data science, and leadership teams to deliver scalable data solutions.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Designs and builds cloud-based, data-centric applications supporting clinical and operational healthcare processes. Develops data pipelines, transformations, enrichment processes, provisioning layers, and user interfaces. Collaborates with Product, Platform, and Architecture teams using modern software development practices, source control, documentation, and regular delivery methods.

Build and maintain recruiting analytics data infrastructure using BigQuery, dbt, Fivetran, SQL, and Python. Design dimensional models, ETL/ELT pipelines, semantic layers, governance controls, data security, monitoring, and standardized recruiting metrics. Integrate Workday, Greenhouse, and internal systems while supporting batch and real-time analytics. Partner with data scientists, engineers, recruiters, and business stakeholders to deliver trusted data products and AI-enabled workforce insights.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Build and operate event-driven data pipelines processing healthcare claims data for fraud, waste, and abuse detection. Operationalize validated fraud signals, maintain alert delivery and partner dashboards, monitor pipeline reliability, and collaborate with data architects, scientists, and investigative teams. The role requires production-scale pipeline engineering using AWS, Snowflake, Python, and SQL in a fully remote environment.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Designs and maintains end-to-end data solutions, including ETL/ELT pipelines, BigQuery platforms, APIs, semantic data layers, and data catalogs. Builds data quality monitoring, governance, security, and access controls while collaborating with analytics, product, engineering, and business teams. Requires strong Python, Kotlin, Java, SQL, API development, cloud data engineering, testing, and reliability experience.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Design, build, and scale data pipelines, ETL workflows, analytics infrastructure, and machine-learning data systems. Ensure data quality, security, monitoring, and reliability while optimizing storage and processing performance. Collaborate with engineering, product, and analytics teams on data requirements, modeling, schemas, and scalable infrastructure. The role also establishes data engineering best practices and supports a data-driven culture.

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 pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and maintain data pipelines, governance documentation, and data architectures supporting Department of State systems. Collect, clean, transform, integrate, validate, and analyze data from multiple sources using Palantir Foundry and relational databases. Collaborate with nontechnical stakeholders to gather requirements, identify data-quality issues, develop reporting tools and visualizations, and recommend improvements supporting data-driven decisions.

Design, build, and optimize complex ETL pipelines, data workflows, monitoring, and alerting systems supporting legal and eDiscovery infrastructure. Lead end-to-end data engineering initiatives, improve query and pipeline performance, document deployment strategies, and ensure privacy, security, and compliance. Collaborate with attorneys, data scientists, engineers, and other stakeholders in regulated environments while contributing to internal consulting and thought leadership initiatives.

Build and optimize scalable ETL pipelines, monitoring and alerting systems, and reliable data solutions for legal and eDiscovery infrastructure. Responsibilities include query and workflow optimization, cross-functional requirements gathering, system documentation, deployment planning, and compliance with data privacy and security standards. The role also contributes to internal consulting, thought leadership, and practice development.

Designs, builds, tests, deploys, and maintains secure ETL/ELT pipelines for public health data. Integrates Power Platform, Azure services, APIs, SQL Server, CDC/NIOSH systems, and approved file-transfer platforms. Develops batch, event-driven, and near-real-time data workflows supporting intake, validation, reporting, analytics, notifications, and results delivery while meeting FISMA Moderate, federal privacy, Agile, and DevSecOps requirements.

Designs and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, Azure, data warehousing, reporting, analytics, and AI/ML workloads. Responsibilities include optimizing performance and reliability, implementing data quality controls, integrating enterprise data sources, modernizing platforms, and partnering with business and IT teams to deliver scalable data solutions.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Designs, builds, and maintains scalable batch and streaming data pipelines in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies warehousing best practices, and partners with business and IT teams to deliver reporting, analytics, and AI/ML data solutions.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.