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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Weyerhaeuser - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $98,811.0 - $148,217.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Automation of Tests, Microsoft Azure, Software as a Service, Cloud Computing, Code Coverage, Code Review, Information Systems, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Relational Databases, Database Schema, JSON, Python (Programming Language), Metadata, Operational Databases, Performance Tuning, Queueing Systems, Raw Data, Power BI, Azure Data Lake, SAP (Applications), SQL Databases, Extensible Markup Language (XML), Azure Service Bus, Data Ingestion, Azure Data Factory, Large Language Models, Snowflake, State Machines, Git, Data Lakes, Information Technology, AWS Glue, Data Analytics, Apache Kafka, Restful APIs, Terraform, Software Version Control, Data Pipelines, Serverless Computing - **Published:** August 6, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/27909726/Data-Engineer-Washington-Seattle-1314 ## About the Role * Bachelor's degree in Computer Science, Information Systems, Engineering or equivalent experience * 4 years of hands-on data engineering experience building and operating production data pipelines from internal and external sources * Strong proficiency in Python (readable, maintainable) ad SQL. * Production experience with a cloud-based ingestion and orchestration platform - Azure Data Factory and Azure Functions preferred, though comparable tools (Fabric Pipelines, AWS Glue/Step Functions, Airflow, Dagster, Prefect, etc.) are acceptable - including parameterized, dynamic, and metadata-driven pipeline patterns. * Production experience with dbt or a comparable transformation framework, including building and choosing across materialization patterns (views, tables, incremental, ephemeral, snapshots), test coverage, documentation, and history preservation. * Production experience with Snowflake or similar data platform: loading patterns, role-based access, performance tuning, and cost-aware design. * Demonstrated experience ingesting from a variety of sources: relational databases, SAP, flat files, REST APIs (JSON/XML), and SaaS applications. * Experience implementing incremental/delta load patterns and managing watermarking, CDC, schema evolution, and backfills. * Working knowledge of Terraform for provisioning Azure and/or Snowflake resources. * Solid understanding of data quality, monitoring, alerting, and operational support practices. * Working proficiency with Git, pull-request workflows, and CI/CD pipelines for data - code review, automated testing, and promotion across environments are part of how you ship. * AI in your engineering workflow - demonstrated use of AI assistants and LLM-powered tools to accelerate development, generate and improve tests, and produce or maintain documentation. * Track record of owning reliability - not just shipping features, but keeping data flowing cleanly over time. * Strong communication skills and the ability to work cross-functionally with engineering, analytics, and business teams. Preferred * Exposure or familiarly working with geo-spatial datasets and using geo-spatial functions * Exposure or familiarity with Iceberg table structures and operations * Experience designing reusable, config-driven ingestion frameworks at scale. * Exposure to streaming or near-real-time ingestion (Event Hubs, Kafka, or similar). * Familiarity with data governance, lineage, and catalog tooling. * Experience with BI tools such as Power BI in a downstream/consumer context. * Experience working with manufacturing, supply chain, or forestry/natural-resources data domains. ## Description Weyerhaeuser's Data & Analytics team is looking for a Data Engineer to build and operate the data platform that powers reporting, analytics, and AI across the enterprise. This hands-on role focuses on building scalable, reliable, well-governed pipelines that move data. We invest heavily in template- and metadata-driven patterns, so onboarding a new source is a configuration exercise, not a net-new build. We expect engineers to use AI as a force multiplier - both in how we build the platform (LLM-assisted development, testing, and documentation) and in what we deliver from it (AI-ready data products grounded in well-modeled sources). This role partners closely with source-system owners, analytics engineers, data scientists, and data analysts. It's well suited for someone who thrives in a fast-paced environment, has strong opinions about data quality and pipeline reliability, and is energized by building scalable foundations rather than one-off integrations. Responsibilities Ingestion & Integration * Design and maintain ingestion pipelines that move data from SAP, relational databases, flat files, REST APIs, message queues, and SaaS applications into our data lake/Snowflake. * Extend our metadata-driven and template-driven ADF pipeline frameworks so onboarding a new source is a configuration exercise - schema mapping, validation, and config, not handwritten pipelines. * Develop Python-based Azure Functions for custom ingestion logic, REST API integrations, paging/retry handling, and schema reconciliation. * Implement reliable full and incremental data load patterns - watermarking, CDC, late-arriving data, and replayable backfills. * Design, develop, and support our geospatial ETL tool data pipelines that ingest, transform, and complex location-based data from enterprise, operational, and third-party sources for analytics and reporting. Modeling & Transformation * Land and preserve history of raw data in the Azure data lake or Snowflake (bronze), then build dbt models that conform, deduplicate, standardize, and enrich it into clean silver datasets. * Partner with analytics engineers and data analysts to build dimensional models and semantic views that enable AI-ready datasets. Orchestration & Reliability * Orchestrate end-to-end workflows in Azure Data Factory - dependencies, parameterization, retries, dynamic parallelism, and error handling for complex multi-source pipelines. * Build monitoring, alerting, and own incident response - triage, root-cause analysis, and backfills, including occasional off-hours coverage for critical loads. * Tune pipelines and Snowflake workloads for performance and cost Data Quality, Security & Governance * Implement data quality rules - schema validation, completeness, freshness, business-rule checks, and anomaly detection - wired into pipelines. * Apply security and compliance best practices and contribute to lineage, metadata, and catalog efforts. Platform & Engineering Practices * Partner with Data Platform Engineers on Terraform-managed cloud resources, and CICD pipelines. * Drive engineering best practices - version control, testing, documentation, observability, and document pipelines, schemas, contracts, and runbooks so the platform is supportable by the broader team. * Mentor junior engineers, contribute to design reviews, and help evaluate new tools and patterns. Contribute to code reviews. AI Enablement * Skilled in the use of AI assistants and LLM-powered tools to accelerate development, generate and improve tests, and produce or maintain documentation. Collaboration * Partner with analytics engineers, data analysts, and data scientists to translate requirements into reliable raw data pipelines they can model into downstream products. * Communicate technical concepts and trade-offs clearly to both technical and non-technical audiences. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence)