Leads development of DataHub's platform framework, building scalable metadata ingestion systems, APIs, event-driven processing, schema mapping, and AI asset versioning. Requires 8+ years in distributed systems, advanced Python/API expertise, and high-scale data processing experience.
225k – 300k/yr
Hybrid8+ YOEBackend Engineering
About the role
Responsibilities
Build scalable, fault-tolerant ingestion systems for enterprise-scale metadata
Develop clean, intuitive APIs for our connector ecosystem
Implement event-driven architectures for real-time metadata processing
Create schema mapping between diverse systems and DataHub's unified model
Design versioning systems for AI assets (training data, model weights, embeddings)
Requirements
8+ years building production-grade distributed systems
Advanced Python and API design expertise
Experience with high-scale data processing or integration frameworks
Strong systems knowledge and distributed architecture experience
Proven track record solving complex technical challenges
Built and maintained online applications serving live traffic at scale (100+ QPS)
Set up monitoring and alerting for services
Designed indexing, storage, and data architectures to make large-scale data accessible to online services
Designed and scaled distributed systems
Hands-on experience developing in a tight loop with LLMs and applying best practices for scalable LLM development
Languages
One of Java/Scala/Kotlin/C#/Go (very strong nice-to-have / borderline must-have)
Python/TypeScript/Node.js (nice-to-have)
Technical Skills
AWS
Kubernetes/Docker
CI/CD deployment pipelines
Microservice Architecture
Nice-to-Haves
Experience with DataHub or similar metadata/ETL frameworks (Airflow, Airbyte, dbt)
Open-source contributions
Experience building and maintaining services that make calls to LLMs in order to serve live traffic
Experience fine-tuning LLM-powered applications exposed to end users
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