Staff Software Engineer | Semantic Data Modeling

About the Role

Key Responsibilities

• Architect and implement large-scale, semantically rich data objects (e.g., Customer 360, Fleet 360) that serve as the single source of truth across domains.
• Translate complex business definitions into scalable, interpretable, and trustworthy data entities used across analytics, AI/ML, APIs, and operational workflows.
• Design for scale, reliability, and performance, handling billions of records and thousands of attributes across sources.
• Build modular, testable, and versioned transformation pipelines with a strong focus on readability, maintainability, and long-term scalability.
• Solve for entity resolution, time-aware modeling, and multi-domain relationships using first-principles thinking and clean abstractions.
• Collaborate with architects, product managers, and domain leads to translate complex business logic into scalable data design patterns.
• Lead design reviews, code quality standards, and performance benchmarking within the semantic modeling engineering team.
• Mentor other engineers and raise the bar for engineering excellence across data systems.

Requirements

• 8+ years of experience in software engineering or data-intensive system design, ideally in environments managing complex, high-volume data ecosystems.
• A systems thinking mindset—you consider data as a platform, not a pipeline.
• Strong understanding of data modeling principles, business semantics, and the challenges of modeling real-world entities at scale.
• Experience building and scaling wide, entity-based tables and modeling domain concepts (e.g., customer, fleet, provider) into durable data objects.
• Solid understanding of data quality practices—including validation, enrichment, schema enforcement, and business rule encoding.
• Experience working with large-scale datasets and optimizing transformation pipelines for performance and maintainability.
• Comfort operating in a collaborative, cross-functional environment, working across product, engineering, and business functions.
• A mindset for traceability, reproducibility, and semantic clarity—you build data models others can trust and reuse.
• Proven experience building and optimizing scalable, distributed systems, including schema design, storage strategy, and lifecycle management.
• Deep focus on code quality, testing, documentation, and version control as foundational engineering practices.

Nice to Have

• Experience with building scalable data systems in cloud environments.
• Knowledge of AI/ML workflows and how semantic data modeling supports them.
• Familiarity with data governance and enterprise data management best practices.

Qualifications

• Educational background or certifications are not explicitly specified in the description.

Benefits & Perks

• Comprehensive and market competitive benefits including health, dental, and vision insurances.
• Retirement savings plan, paid time off, health savings account, flexible spending accounts.
• Life insurance, disability insurance, tuition reimbursement, and more.