Principal Data Engineer

readyon.ai•San Francisco, CA

9h•Onsite

About The Position

ReadyOn is seeking a Principal Data Engineer to join their top-tier engineering team. This role 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. The ideal candidate is a hands-on senior engineer who thrives in ambiguous, high-impact environments, naturally sets technical direction, and cares deeply about clean system design, scalability, and elegant architecture. They will enjoy working closely with product, design, and AI research teams to deliver new data-driven experiences, focusing on business outcomes and solving real business problems with data, services, and automation.

Requirements

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.

Nice To Haves

Hands-on experience with conflict resolution in collaborative or concurrent-editing systems, graph processing, feature stores, or real-time coordination tools and algorithms.

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.