Senior Software Engineer - Python and Data Ecosystem

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.

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.

Responsibilities

• 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.