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
