Senior Software Engineer - Python and Data Ecosystem

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies proce WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards - 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 - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including 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 Bonus points for: - 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 you'll do - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards - 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 - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, ideally with 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) - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect) - Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation - Strong understanding of 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 skills; comfortable collaborating across engineering functions and with open-source communities Bonus points for: - Experience deploying AI/ML models in production, including inference APIs and vector databases - 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     Eligible locations: - USA - Canada - UK - Portugal - Spain - France - Italy - Germany - Netherlands - Poland - Czech republic - Israel - UAE