Data Engineer

Weyerhaeuser•Seattle, WA

10h•$98,811 - $148,217•Onsite

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering
4+ years of hands-on data engineering experience building and operating production data pipelines.
Strong proficiency in SQL (including performance tuning) and Python (readable, testable, maintainable code).
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
Skilled in the use of AI assistants and LLM-powered tools to accelerate development, generate and improve tests, and produce or maintain documentation.
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.