Data Engineering Tech Lead
Cosmose AI provides Attention-as-a-Service using first-party data and proprietary AI models. Its products deliver personalized experiences through smartphone lock screens and other digital touchpoints for brands and businesses.
Funding history
Investors
About Cosmose AI
Cosmose AI is an artificial intelligence company focused on attention and personalization. Its proprietary MindMentor AI engine powers personalized lock-screen content through on-device computing, emphasizing privacy and keeping personal data on the device. The company serves luxury brands, FMCG companies, automotive companies, and e-commerce businesses with AI-powered solutions designed to improve audience engagement and growth.
Skills
About the Role
You will own the technical direction of a data platform, make architectural decisions, write production code, solve distributed-systems problems, optimize performance and cost, establish data standards and quality practices, and lead a small team through technical direction, mentorship, reviews, and hiring.
Requirements
- Production software engineering experience with Python or Java
- Deep understanding of distributed systems
- Data engineering experience with pipelines, data platforms, warehouses or lakes, and data modeling
- Ability to make architectural decisions independently
- Ability to identify problems and opportunities independently
- High standards for performance, reliability, cost, data quality, and engineering discipline
- Polish and English language proficiency
- Ability to learn new technologies quickly
- Experience or interest in Flink, ClickHouse, Iceberg, Spark, Python, or Java
Responsibilities
- Own the technical direction of the data platform
- Make architectural decisions and evaluate their long-term consequences
- Design systems and write production code
- Review implementations and solve complex technical problems
- Drive initiatives from problem identification through production
- Optimize platform performance and cost
- Establish data contracts, schemas, ownership, documentation, standards, and quality practices
- Solve distributed-systems problems involving consistency, fault tolerance, schema evolution, streaming, storage, scalability, and performance
- Lead and grow the data engineering team through technical direction, reviews, mentorship, and one-to-ones
Benefits
- Stock options