Senior Business Intelligence Analytics Engineer

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.

Weyerhaeuser is investing in a modern analytics foundation that turns operational and enterprise data into trusted, reusable insights the business can act on. We’re hiring a Senior Analytics Engineer to design and build the data pipelines, semantic models, and analytical reports that bring that foundation to life — and to do it with a product mindset, so what you build is durable, well-adopted, and ready for both human and AI consumers. This is a deeply hands-on engineering role. You’ll spend most of your time modeling data, writing SQL and dbt, building semantic models, and shipping Power BI reports that people actually use. Alongside that, you’ll own the set of data products you build — the roadmap, the rollout, and the relationship with the consumers who depend on them — so the engineering work translates into real business adoption. You’ll partner with business stakeholders, data scientists, data engineers, and governance teams, and you’ll engineer the semantic foundation that lets people and AI agents reason consistently over our business data. If you’re energized by building durable systems, raising the bar for engineering rigor, and seeing your work land with the teams that depend on it — we’d like to talk.

Analytics Engineering & BI - Design and build performant, well-tested transformation pipelines using SQL, dbt (or equivalent), and modern ELT patterns on our cloud data platform. - Apply dimensional modeling and domain-driven patterns based on what the use case actually needs. Champion consistency in naming, grain, and definitions. - Build and maintain Power BI semantic models with clean, certified metric definitions. - Design and develop Power BI reports and dashboards — partnering directly with consumers on layout, calculations, and the questions each report needs to answer. - Implement testing, lineage, and observability so data quality issues are caught before consumers find them. Tune for cost and performance, and refactor when models outgrow their original design. - Apply automation and LLM-assisted workflows to accelerate modeling, documentation, and quality testing where it makes sense. Data Product Ownership & Delivery - Own the roadmap and lifecycle of the data products you build — datasets, semantic models, and reports — from initial scoping through iteration and eventual retirement. - Own the rollout and delivery of each data product to its consumers: onboarding, training, communication, and ongoing support that turn what you build into real adoption. - Maintain quality and freshness expectations with consumers, and respond when something slips. - Gather usage signals and feedback to inform what to invest in next — and what to retire. Governance & Quality - Partner with Data Governance on certified datasets, master data alignment, sensitivity classification, lineage, and stewardship workflows. - Champion high data quality and operational excellence as a default, not an afterthought. - Follow change management processes and procedures in-line with IT controls. Engineering Maturity - Review peers’ code, models, and reports. Mentor analytics engineers, and data analysts on modeling, SQL, DAX, testing, and product thinking. - Contribute to