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 process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.
About the role
As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.
We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.
You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:
Orchestration Platforms: Apache Airflow, Dagster, Prefect
Transformation Tools: dbt, SQLMesh
AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications
ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real: building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.
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
