Data Engineer

ABOUT OnX We’re a team of builders, adventurers, and risk takers using technology to help people confidently explore the outdoors. Driven by our mission to awaken the adventurer inside everyone, we build products that optimize every outdoor experience and inspire confidence to get out and go further.

We’re a high-growth tech company. The pace is fast, the work takes grit, and ambiguity is part of the job. As the world changes around us, we adapt – continuously evolving how we build, prioritize, and deliver. Our business moves quickly, and there’s real opportunity to shape what we build next. Each of our verticals – Hunt, Offroad, Backcountry, and Fish – is at a different stage of maturity, which means the challenges you encounter and the impact you have will vary depending on where you sit and what the business needs most.

We operate with an experimentation mindset, continually iterating and improving how we solve problems. We expect our people to use the latest tooling, including AI, thoughtfully and responsibly, pairing human judgment with technology to increase quality, speed, and impact. Our impact comes to life through the products we build, in the stories of our customers, and in our growing commitment to land stewardship and recreational access.

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 s 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.