Used Tools & Technologies
Machine LearningRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Software Development @ 7
Kafka @ 4
Python @ 4
SQL @ 3
Spark @ 4
Tableau @ 4
dbt @ 4
Airflow @ 4
Flink @ 4
JVM @ 3
MLOps @ 7
Communication @ 4
Dagster @ 4
Data Engineering @ 4
Prioritization @ 4
API @ 4
BI @ 7
LLM @ 4
Fivetran @ 4
OLAP @ 3
Pandas @ 4
Observability @ 4
AI @ 4
Data Visualization @ 4
Data Modeling @ 3
ClickHouse @ 4
RAG @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
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 company’s momentum was recently validated by a $400M Series D financing round. Customers include Capital One, Lovable, Decagon, Polymarket, Airwallex, Meta, Cursor, Sony, and Tesla.
The Connectors team builds and maintains integrations that make ClickHouse accessible across data visualization plugins (Tableau, PowerBI, Superset, Metabase), data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.
Role overview
You will 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, crafting tools that enable Data Engineers and Data Scientists to use ClickHouse from the frameworks they already use.
You will focus on production-ready Python integrations and connectors that enable RAG pipelines, vector stores, ML feature stores, and LLM-powered data applications.
Responsibilities
- Own and evolve ClickHouse's Python connector and SDK ecosystem, improving performance, reliability, and API design
- Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade standards
- Drive AI/LLM integration strategy: design connectors and patterns for RAG architectures, embedding pipelines, vector search, and feature pipelines
- Engage with the open-source community: triage issues, support contributors, advocate for users, and shape roadmap from real-world feedback
- Collaborate with Product, Cloud, and other engineering teams to align integration work with platform priorities
- Bring practitioner experience (Data Engineer/Data Scientist) to roadmap and prioritization
Requirements
- 7+ years of software development experience; ideally hands-on 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 data orchestration tools such as Apache 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
- 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
- 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
Compensation
For roles based in the United States, the typical starting salary range for this position is:
- $141,000 - $208,000 USD (US typical range)
- $157,000 - $232,000 USD (US Premium Markets such as San Francisco Bay Area and NYC Metro)
Placement within the range depends on education, experience, skills, location, and other factors. For compensation questions: [email protected]
Perks
- Flexible work environment — globally distributed and remote-friendly
- Employer contributions towards healthcare
- Equity (stock options) for new team members
- Flexible time off in the US; generous entitlement in other countries
- $500 home office setup for remote employees
- Global company-wide offsites
Culture & Privacy
As part of the first 500 employees, you'll help shape company culture. ClickHouse is an equal opportunity employer. See the applicant privacy notice on the company site for details.