Data Engineer

Data Engineer San Francisco Engineering In office Full-time Company Overview ReadyOn.AI is an AI-native Labor Operating System that is redefining how the world’s largest enterprises manage frontline labor. Born out of a Stanford AI Lab, the company applies advanced AI and market-design principles to one of the hardest optimization problems on earth: matching the world’s 2.7 billion frontline workers to the right shifts, in real time. Frontline workers now expect the same flexibility and autonomy that gig platforms provide, while large employers face relentless pressure to meet aggressive labor-cost targets. ReadyOn bridges that divide with a system of action that predicts workforce demand, dynamically matches it to an employer’s supply of employees, and automates the thousands of staffing decisions made daily across complex, multi-site operations. The platform is already proven at global scale, powering labor operations for several of the world’s largest enterprises. Landmark customers include a F250 food-service enterprise (300K employees across 16 countries; $7B+ annual labor spend), a F500 hotel group (250K+ employees; $5B+ annual labor spend), a F250 entertainment operator (75K employees; $4B+ labor spend). Across these deployments, ReadyOn has proven that scheduling was never the real problem—it was a symptom. The true challenge is how to match people and work dynamically at scale. ReadyOn solves this problem with an AI system of action that transforms labor from a fixed cost into a strategic advantage, reshaping how enterprises think about workforce design altogether. Headquartered in San Francisco with 80 employees, ReadyOn grew 8x year-over-year revenue growth in 2025, driven by multiple seven‑figure Fortune 250 enterprise deployments and a rapidly expanding pipeline. Transform How Frontline Work Runs Enterprises struggle to manage hundreds of millions of dollars in frontline labor spend due to decades-old software and manual processes, creating massive, avoidable costs. Frontline labor often represents 40% of the P&L, yet the systems managing this $3 trillion market were built for static schedules and limited flexibility. ReadyOn was founded to reject that paradigm. Staffing is not a scheduling problem; it is a real‑time supply–demand orchestration problem. ReadyOn is an AI-native labor operating system, built from the ground up for AI agents to perform real-time labor optimization - much like ridesharing platforms that match drivers and riders in real time, but applied to frontline labor instead of fixed, one‑size‑fits‑all schedules. Who’s Building It AI is not a bolt‑on feature in our platform. Every decision, from demand forecasting to shift assignment, flows through an adaptive, autonomous decision layer that learns from operational data and continuously optimizes for cost, compliance, and worker satisfaction. Behind that system is a founding team of experts in labor markets, enterprise software, and AI-enabled platforms: Reza – Engineering leader who scaled enterprise systems at Google, Yahoo, and AT&T Dominic – Operator who optimized labor‑intensive operations in 21 countries Mohammad – Stanford professor and leading expert in algorithmic market design ReadyOn has already proven product–market fit with multiple multi‑million‑dollar customers, consistent expansion within existing accounts, and measurable ROI that moves stock prices. Hands‑On Builders Leading AI Innovation We’re building a top‑tier engineering team to reimagine how labor is managed at scale. As a Data Engineer, you will own the data platform and core data services that power ReadyOn’s real‑time labor operating system, enabling AI agents to orchestrate frontline work across thousands of shifts, locations, and workers every day. Ideal candidates Are hands‑on senior engineers who thrive in ambiguous, high‑impact environments and naturally set technical direction for others. Care deeply about clean system design, scalability, and elegant architecture across both data and backend systems, and are not afraid to rethink default patterns. Enjoy working closely with product, design, and AI research teams to deliver new data‑driven experiences customers actually use. Focus on business outcomes, not just technical output, and love solving real business problems with data, services, and automation. Responsibilities Design, build, and scale data pipelines and data services using Python, TypeScript, Apache Airflow, PySpark, AWS Glue, and Snowflake to support both real‑time and batch workloads. Design, operationalize, and monitor ingest and transformation workflows, including DAGs, alerting, retries, SLAs, and robust data quality checks for production environments. Collaborate with AI, platform, and backend teams to automate ingestion, data validation, and real‑time compute workflows, and drive the roadmap toward a production‑grade feature store that supports AI agents and decisioning. Partner closely with the core engineering team to shape ReadyOn’s Integration Platform, ensuring external systems (HCM, WFM, payroll, timekeeping, and other enterprise tools) integrate cleanly and are observable end to end in ReadyOn dashboards. Model data structures and implement efficient, scalable transformations in Snowflake and PostgreSQL, including schema design, indexing, partitioning, and query optimization for high‑volume, low‑latency use cases. Build reusable frameworks, connectors, and internal libraries that standardize how data is published, discovered, and consumed by backend services, analytics, and AI workloads. Implement and continuously improve observability across pipelines and services: structured logging, metrics, tracing, data quality monitoring, lineage, and incident response playbooks. Provide technical leadership on data and backend integration: participate in system design and code reviews, mentor other engineers, and help drive sound, pragmatic technical decisions in a fast‑moving environment. Your background 5 plus years of production data engineering experience, including owning critical pipelines, datasets, and services in live environments. Deep, hands‑on experience with Apache Airflow, AWS Glue, PySpark, and Python-based data pipelines, including orchestration, monitoring, and troubleshooting at scale. Solid SQL skills and experience working with PostgreSQL in production: schema design, query optimization, migration management, and handling concurrency in large‑scale environments. Strong understanding of cloud‑native data and service workflows (AWS preferred), including data warehousing, storage, security, and cost‑efficient architectures. Fluency in TypeScript and experience with a backend framework such as NestJS (or other Node.js frameworks), including designing decoupled services and robust enterprise interfaces; GraphQL experience is a significant plus. Experience implementing observability for data and backend systems: logging, metrics, tracing, data validation, and automated alerts for pipeline and service health. Comfortable collaborating with AI/ML and data science teams, understanding how data flows into models, feature stores, and real‑time decisioning workflows, even if you are not a data scientist yourself. Bonus: hands‑on experience with conflict resolution in collaborative or concurrent‑editing systems, graph processing, feature stores, or real‑time coordination tools and algorithms. If you’re looking for predictability, rigid structure, or narrow specialization, this probably isn’t the right role. This is a principal‑level position for hands‑on builders who want to define the data foundation of an AI-native labor operating system and shape how data, AI, and backend services come together in production. #J-18808-Ljbffr