Staff Data Engineer

Responsibilities

Technical Leadership & Architecture

• 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.
• Evaluate and integrate technologies (build vs. buy).

Metadata, Governance & Open Standards

• 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., OpenMetadata).
• 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.

Security, Access Control & Compliance

• 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.

Platform Reliability & Operations

• 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.

Organizational Impact & Collaboration

• Align technical work with business priorities by understanding how data supports onX products and customer outcomes.
• Communicate complex technical concepts clearly to engineers, product partners, and leadership.
• Lead and participate in architecture and design reviews, setting a high bar for technical rigor.
• Foster strong cross-team collaboration across Data Engineering, Platform, Security, Analytics, and Data Science.
• Mentor senior and mid-level engineers, raising the technical bar across the team.

Requirements

Required

• Bachelor’s degree in Computer Science or equivalent experience.
• Deep industry experience (typically 12+ years) building and operating large-scale data systems.
• Deep expertise in distributed data systems and data architecture.
• Strong experience with Apache Iceberg and similar table formats (Delta Lake, Hudi).
• Proven experience designing secure and governed data platforms.
• Expertise in Python, SQL, and orchestration patterns (e.g., Airflow).
• Experience working with data ecosystems, including metadata, catalog, or governance tooling.
• Strong written and verbal communication skills.
• Permanent U.S. work authorization.

Cloud & Platform Experience

• Deep experience in at least one major cloud environment (GCP, AWS, or Azure).
• Familiarity with cloud-native data services such as query engines, stream/batch processing systems, and object storage–based lakehouses.
• Comfort with infrastructure-as-code and automated platform management.

Compensation

• Base salary: $175,000 - $218,000 upon hire (varies based on experience, skills, certifications, and education).
• Full-time employees eligible for common share options (vesting schedule) and potential annual bonus of 10% based on company performance.