Principal Data Engineer

Transform How Frontline Work Runs

Who’s Building It 

Hands-On Builders Leading AI Innovation

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.​

Interview Process

1. Screening call with Talent (Recruiter)
2. 1:1 interview with Founder/CTO, Reza Iranmanesh (hiring manager)
3. Technical panel: cross-functional technical interview (virtual)
4. Onsite interview with direct team and select founding members

Location: Why In-Person Matters

Compensation

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