Data Engineer - Bozeman

ABOUT onX

As a pioneer in digital outdoor navigation with a suite of apps, onX was founded in Montana, which in turn has inspired our mission to awaken the adventurer inside everyone. With more than 400 employees located around the country working in largely remote / hybrid roles, we have created regional “Basecamps” to help remote employees find connection and inspiration with other onXers.

We bring our outdoor passion to work every day, coupling it with industry-leading technology to craft dynamic outdoor experiences.

Through multiple years of growth, we haven’t lost our entrepreneurial ethos  offer a fast-paced, growing, tech-forward environment where ownership, accountability, and passion for winning as a team are essential. We value diversity and believe it leads to different perspectives and inspires both new adventures and new growth. As a team, we’re hungry to improve, value innovation, and believe great ideas come from any direction.

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The Staff Data Engineer is a senior leader responsible for designing and evolving core components of onX’s lakehouse and data platform. This role focuses on how data is structured, governed, secured, and described so that analytics, product features, and AI systems can operate reliably at scale.

This engineer operates at the intersection of data architecture, metadata, governance, and security, leading complex initiatives and setting technical direction within the Data Engineering organization. They are a trusted technical partner to Product, Analytics, Data Science, Security, and Platform teams, and serve as a force multiplier for other engineers through high-level technical guidance and active mentorship.

• Design and evolve the Iceberg-based lakehouse architecture to balance scalability, cost, performance, and maintainability.
• Define and promote standards for table design, partitioning, schema evolution, optimization, and data layout.
• Lead architectural efforts spanning batch, streaming, and event-driven data processing where they deliver business value.
• Drive the design and delivery of complex, cross-team initiatives, enabling teams to move independently within established architectural guidance.
• Build vs. Buy:
  Evaluate and integrate technologies.

• Define how datasets, pipelines, features, and models are described, related, and governed using shared metadata.
• Lead the adoption and integration of open-source metadata and catalog tools (e.g., Open Metadata or similar ecosystems).
• Establish metadata standards that enable self-service analytics, governance, and AI readiness.
• Partner with BI and Analytics to ensure domain models are clearly documented and aligned to business language.
• Collaborate with Data Science to ensure model inputs, features, and outputs are traceable, explainable, and reusable.

• Design and evolve security and access-control models for Apache Iceberg, including table-, column-, and row-level controls.
• Partner with Security and Platform teams to embed policy enforcement directly into data access paths.
• Drive metadata-driven authorization patterns that scale across tools and user groups.
• Ensure privacy, compliance, and regulatory requirements are incorporated into platform design.
• Balance strong security guarantees with usability to support safe self-service.

• Build and maintain automation for compaction, retention, lifecycle management, and cost controls.
• Establish observability standards that connect pipeline health, data quality, and reliability metrics.
• Provide architectural oversight during critical incidents and drive long-term Keep the Lights On (KTLO) reduction.
• Recommend tooling and process improvements based on industry standards and operational experience.

• Align…