Data Engineer

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.