What You’ll Have

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.

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.

Location\: This role will be based out of our corporate office in Seattle, WA.

What We Offer:

Compensation\: This role is eligible for our annual merit-increase program, and we are targeting a salary range of $98,811-$148,217 based on your level of skills, qualifications and experience. You will also be eligible for our Annual Incentive Program, which offers a cash bonus targeting 10% of base pay. Potential plan funding may range from zero to two times that target.

Benefits\: When you join our team, you and your dependents will be offered coverage under our comprehensive employee benefits plan, which includes medical, dental, vision, short and long-term disability, and life insurance. We offer a pre-tax Health Savings Account option which includes a company contribution. Other benefit options are also available such as voluntary Long-Term Care and Employee Assistance Programs. We also support personal volunteerism, sponsor a host of diversity networks, promote mentoring, and provide training and development opportunities to help you chart your path to a fulfilling career.

Retirement: Employees are able to enroll in our company’s 401k plan, which includes a paid company match in addition to our annual contribution equal to 5% of your base salary.

Paid Time Off or Vacation\: We provide eligible employees who are scheduled to work 25 hours or more per week with 3-weeks of paid vacation to use during your first year of employment. In addition, after being employed for six months, eligible employees begin to accrue vacation for future use. We also recognize eleven paid holidays per year, providing a total of 88 holiday hours and paid parental leave for all full-time employees.

Weyerhaeuser is an equal opportunity employer. Inclusion is one of our five core values and we strive to maintain a culture where all our people feel a sense of belonging, opportunity and shared purpose. We are committed to recruiting a diverse workforce and supporting an equitable and inclusive environment that inspires people of all backgrounds to join, stay and thrive with our team.

About Weyerhaeuser

At Weyerhaeuser, we are the world’s premier timber, land, and forest products company. Sustainability is the founding concept of our business and our values drive every decision to ensure we continue to lead the forestry industry in sustainability practices. And we know about sustainability – we led it in the forestry industry when we planted our first seedling by hand in 1938. We recognize that our success is dependent on the success of our people. For over 125 years, our Weyerhaeuser team has been making a difference in the world – from the seedlings we plant, to the forests and trees we nurture, we ensure every acre is managed with diligence, patience and pride. That’s the Weyerhaeuser way.

About the Role

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.

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.

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