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 agents. Requires strong Python, data engineering, and production AI/ML experience.

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

  • 7+ years of software development experience
  • hands-on time as a Data Engineer, Data Scientist, or ML Engineer
  • Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
  • Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production
  • Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
  • Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
  • Solid experience with concurrent Python: threading, multiprocessing, and async patterns
  • Outstanding written and verbal communication

Nice to have

  • Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
  • Familiarity with ClickHouse or similar high-performance OLAP platforms
  • Familiarity with the JVM ecosystem
  • Experience deploying AI/ML models in production, including inference APIs and vector databases

What the JD emphasized

  • lived the Data Engineer or Data Scientist experience firsthand
  • operated within them
  • product-level insight
  • production-grade Python connectors, SDKs, or integrations
  • applying AI/ML in production data-engineering contexts
  • shipped and ran in production

Other signals

  • AI-powered workflows
  • vector stores for RAG pipelines
  • backends for LLM-powered agents
  • ML feature stores
  • LLM-powered data applications
  • embedding generation
  • vector search
  • ML feature pipelines
  • LLM-powered tooling