Manager Data Analytics Engineer Jobs in Oregon (NOW HIRING)

We're looking for a Director of Business Analytics Engineering to lead and grow the data modeling function within our Business Data & Analytics organization - a player/coach who thrives at the intersection of technical depth and people leadership. You'll own the data models, transformation layer, and semantic foundation that power business decision-making, while building and mentoring a high-performing team of analytics engineers.

This role sits at the core of how our internal business stakeholders - including Finance and the Data Analysts who support them - trust and use data. You'll partner closely with Finance-aligned Data Analysts to understand their analytical needs and translate those into clean, well-governed, reusable data assets. Our BI Engineering team owns the data ingestion pipelines and stack administration; your lane is the semantic layer and data models built on top of that foundation.

• Technical Leadership:
• Design and own a scalable, well-documented semantic layer that serves as the authoritative source of truth for business metrics and KPIs - the foundation Finance and Data Analysts rely on daily.
• Establish data engineering best practices, modeling standards, and data quality controls across the organization.
• Own and evolve our dbt + Snowflake transformation layer, including modeling standards, testing frameworks, documentation practices, and CI/CD workflows.
• Drive data quality; build automated testing, alerting, and observability practices that give business stakeholders confidence in the numbers.
• Partner with BI Engineering on the handoff between ingested data and modeled data, ensuring clean interfaces and clear ownership boundaries.
• Ensure Tableau dashboards and Finance-facing self-service analytics are powered by clean, performant, well-modeled data assets.
• People Leadership:
• Hire, develop, and retain a team of analytics engineers, setting clear expectations and career growth paths.
• Foster a culture of craft - high standards for code quality, peer review, documentation, and knowledge sharing.
• Partner with Finance leadership and their Data Analysts to deeply understand business data needs and translate them into the modeling roadmap.
• Serve as a technical escalation point and hands-on contributor when the work demands it.
• Strategy & Stakeholder Engagement:
• Serve as the primary data modeling and engineering partner to Finance and other internal business functions - translating analytical requirements into durable, reusable models that analysts can self-serve against.
• Assist in establishing company-wide standards for metric definitions, data governance, documentation, and data stewardship across business functions.
• Define the Business Analytics Engineering roadmap and communicate priorities and progress to senior leadership.
• Champion data governance, lineage, and trust - ensuring business stakeholders always know which numbers to trust and why.
• Collaborate on shared standards while maintaining clear ownership of the modeling and semantic layer domain.
• Evaluate and adopt tooling and best practices as the business data ecosystem evolves.

• 7+ years of experience in data, with 3+ years in analytics engineering or a closely adjacent function.
• 5+ years of people management experience; you've built and developed teams, not just led them.
• Deep, hands-on expertise with dbt - you've built production-grade dbt projects and can speak fluently to modeling patterns, testing, macros, and CI/CD.
• Strong Snowflake proficiency and ecosystem - query optimization, warehousing strategy, cost management.
• Experience partnering with Finance, FP&A, or business operations teams - you understand the domain and can speak their language as fluently as SQL.
• Fluency with Tableau or comparable BI tools; you understand what good data modeling looks like from the consumer's perspective.
• Track record of building and maintaining semantic/metrics layers and defining organizational standards for KPIs.
• Strong communication skills - equally comfortable in a dbt PR review and a presentation to the VP of Finance.

• Familiarity with or experience in SaaS business models and related metrics.
• Experience with data observability tooling (Monte Carlo, Elementary, etc.).
• Familiarity with data mesh, data products, or federated ownership models.
• Exposure to ML feature engineering or working alongside Data Science teams.
• Background in a high-growth or scaling analytics environment.

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