12 months ago
Responsibilities
- Design, build, and scale real-time and batch data pipelines and data services using Python, TypeScript, Apache Airflow, PySpark, AWS Glue, and Snowflake.
- Operationalize and monitor ingestion and transformation workflows with DAGs, alerting, retries, SLAs, and data quality checks.
- Collaborate with AI, platform, and backend teams on ingestion, validation, real-time compute, and a production-grade feature store for AI agents and decisioning.
- Shape the Integration Platform and connect HCM, WFM, payroll, timekeeping, and other enterprise systems with end-to-end observability.
- Design data structures and scalable transformations in Snowflake and PostgreSQL, including schema design, indexing, partitioning, migrations, concurrency, and query optimization.
- Build reusable frameworks, connectors, and internal libraries for publishing, discovering, and consuming data across backend, analytics, and AI workloads.
- Improve observability through structured logging, metrics, tracing, lineage, data quality monitoring, and incident response playbooks.
- Provide technical leadership through system design, code reviews, mentoring, and pragmatic architectural decisions.
Requirements
- At least 5 years of production data engineering experience owning critical pipelines, datasets, and services.
- Deep hands-on experience with Apache Airflow, AWS Glue, PySpark, and Python data pipelines, including orchestration, monitoring, and troubleshooting at scale.
- Strong SQL and production PostgreSQL experience covering schema design, query optimization, migration management, and concurrency.
- Understanding of cloud-native data and service workflows, data warehousing, storage, security, and cost-efficient architectures; AWS is preferred.
- Fluency in TypeScript and experience with NestJS or another Node.js backend framework for decoupled services and enterprise interfaces.
- Experience implementing observability for data and backend systems, including logging, metrics, tracing, data validation, and automated alerts.
- Ability to collaborate with AI/ML and data science teams on data flows into models, feature stores, and real-time decisioning.
- Preferred experience includes GraphQL, conflict resolution in collaborative or concurrent-editing systems, graph processing, feature stores, or real-time coordination tools and algorithms.
- Hands-on senior engineering capability with technical direction, system design, scalability, clean architecture, mentoring, and cross-functional collaboration.
Benefits
- The role may include comprehensive benefits, bonuses, commissions, and meaningful equity.
- This is an onsite position in ReadyOn’s San Francisco office, Monday through Friday.
- The interview process includes a recruiter screening, founder/CTO interview, virtual technical panel, and onsite interviews.
Tech Stack
Categories
BackendData Engineering
