Senior Analytics Engineer
About The Position
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.
Requirements
- 7+ years of experience in analytics engineering, BI engineering, or related field.
- Demonstrated ownership of one or more data products or analytics domains from concept through ongoing operation — not just project-based delivery.
- Track record of partnering directly with business stakeholders to shape requirements and roadmaps, not just receive them.
- Expert SQL, including complex transformations, window functions, performance tuning, and query optimization on large datasets.
- Strong hands-on experience with a modern transformation framework (dbt strongly preferred) and version-controlled analytics workflows (Git, code reviews, CI/CD).
- AI-ready data — building datasets and semantic models so AI agents and LLM tools can ground their answers reliably (clear definitions, consistent grain, rich metadata, contextual descriptions, predictable behavior).
- 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.
- Proficiency with at least one cloud data platform: Snowflake, Databricks, Azure Synapse, BigQuery, or comparable.
- Solid grasp of dimensional modeling and data warehousing fundamentals (Kimball, data vault, or domain-driven equivalents).
- Strong hands-on Power BI experience — building semantic models, writing DAX, and developing reports and dashboards consumers actually use.
- Working proficiency in Python (or comparable) for automation, orchestration, and data quality tooling.
- Familiarity with orchestration tools (Airflow, Azure Data Factory, Prefect, Dagster) and data quality / observability tools (dbt tests, Great Expectations, Monte Carlo, or similar).
- Data-as-a-product mindset: you frame work in terms of consumers, adoption, reliability, and outcomes — not just tables shipped. You care about the experience of data for both human teammates and AI agents.
- Strong written and verbal communication. You can explain trade-offs to engineers and to non-technical executives without losing either audience.
- Comfort leading through influence across business, engineering, and governance stakeholders.
- Bias for documentation, clarity, and durable decisions.
- Experience working in an Agile delivery framework (Scrum or Kanban) using tools like Jira or Azure DevOps.
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, or a related field — or equivalent practical experience.
Nice To Haves
- Experience in forest products, manufacturing, supply chain, or other asset-heavy industries.
- Experience with data catalog and governance tooling (Microsoft Purview, Collibra, etc,).
- Experience grounding LLMs or AI agents on enterprise data — retrieval, semantic search, or agent workflows in production.
Responsibilities
- 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.
- 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.
- 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.
- Review peers’ code, models, and reports. Mentor analytics engineers, and data analysts on modeling, SQL, DAX, testing, and product thinking.
- Contribute to platform standards: style guides, CI/CD for analytics code, deployment patterns, semantic model and report governance.
Benefits
- medical
- dental
- vision
- short and long-term disability
- life insurance
- pre-tax Health Savings Account option which includes a company contribution
- voluntary Long-Term Care
- Employee Assistance Programs
- personal volunteerism
- diversity networks
- mentoring
- training and development opportunities
- 401k plan
- paid company match in addition to our annual contribution equal to 5%-25% of your base salary
- 3-weeks of paid vacation to use during your first year of employment
- paid parental leave
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What This Job Offers
Job Type
Full-time
Career Level
Senior
