About ClickHouse
Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads.
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 to connectors for data frameworks, orchestration platforms, and AI tooling.
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.