Senior Software Engineer - Python and Data Ecosystem

ClickHouse ClickHouse · Data AI · Israel +2 · Engineering

Senior Software Engineer focused on Python and the data ecosystem, specifically building and evolving ClickHouse's Python connector and SDK. The role involves creating integrations with orchestration and transformation tools, driving the AI/LLM integration strategy for RAG architectures and ML feature pipelines, and engaging with the open-source community. Requires significant software development experience, including hands-on time as a Data Engineer, Data Scientist, or ML Engineer, with proven experience in production-grade Python connectors and applying AI/ML in production data-engineering 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

  • 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; comfortable collaborating across engineering functions and with open-source communities

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

  • production-grade Python connectors
  • applying AI/ML in production data-engineering contexts
  • embedding generation
  • vector search
  • LLM-powered tooling that shipped and ran in production

Other signals

  • AI/LLM integration strategy
  • connectors and patterns for RAG architectures
  • ML feature pipelines
  • LLM-powered data applications
  • embedding generation
  • vector search