Senior Software Engineer - Python and Data Ecosystem

ClickHouse ClickHouse · Data AI · Israel +2 · Engineering

Senior Software Engineer to build and maintain Python integrations for ClickHouse, focusing on data ecosystem and AI/LLM workflows like RAG, feature stores, and LLM-powered applications. The role requires hands-on experience as a Data Engineer or Data Scientist and a deep understanding of production AI/ML contexts.

What you'd actually do

  1. Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
  2. Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
  3. Drive the AI/LLM integration strategy: designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
  4. Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
  5. Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities

Skills

Required

  • Python
  • Data Engineering
  • Data Science
  • ML Engineering
  • Python connectors
  • SDKs
  • integrations
  • orchestration platforms
  • transformation tools
  • AI/ML production contexts
  • embedding generation
  • vector search
  • feature pipelines
  • LLM-powered tooling
  • Pandas
  • NumPy
  • Pydantic
  • SQL
  • data modeling
  • query optimization
  • OLAP databases
  • concurrent Python
  • threading
  • multiprocessing
  • async patterns
  • written and verbal communication

Nice to have

  • ClickHouse
  • high-performance OLAP platforms
  • JVM ecosystem
  • deploying AI/ML models
  • inference APIs
  • vector databases

What the JD emphasized

  • lived the Data Engineer or Data Scientist experience firsthand
  • operated within them
  • product-level insight
  • hands-on time as a Data Engineer, Data Scientist, or ML Engineer
  • Hands-on experience applying AI/ML in production data-engineering contexts

Other signals

  • building integrations for AI agents
  • ClickHouse as a vector store for RAG
  • ML feature stores
  • LLM-powered data applications