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Data Infrastructure Engineer (Query Engine)
Zaimler
San Mateo, USA
Full-time
Senior Level
Onsite
Discovered 1 weeks ago
Free
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- Thrives in early-stage environments, eager to build robust systems from scratch.
- Passionate about distributed systems and solving complex data challenges at scale.
- Able to navigate ambiguity and adapt to changing requirements in a fast-paced startup.
- Advocates for engineering efficiency and continuous improvement.
- A leader who enjoys mentoring others and fostering a strong engineering culture.
- Excited to work cross-functionally with a team that values transparency, purpose-driven innovation, and collective leadership.
- Design and Develop: Build scalable, fault-tolerant query engines optimized for performance and resource efficiency.
- Optimize Performance: Apply advanced techniques such as vectorized processing, cost-based optimization, and caching to enhance query execution.
- Integrate Seamlessly: Develop integrations with modern data lake formats (e.g., Apache Iceberg, Delta Lake, and Hudi) and semantic layers.
- Innovate in Distributed Systems: Architect solutions to handle concurrency, scalability, and reliability in distributed environments.
- Leverage Open Source: Contribute to or extend platforms like Apache Spark, Presto, and Trino to meet unique product requirements.
- Collaborate: Work with product, data science, and engineering teams to align technical solutions with business needs.
- Stay Current: Research and implement the latest advancements in query processing and distributed systems.
- Proficiency in programming languages such as Java, Scala, Rust, or C++.
- Deep understanding of query engine internals, distributed systems architecture, and parallel query processing.
- Experience with modern big data technologies (e.g., Apache Spark, Presto, Trino) and data formats like Parquet, ORC, or Avro.
- Proven ability to build and optimize scalable systems capable of processing petabyte-scale datasets.
- Strong grasp of SQL semantics, execution plans, and query optimization techniques.
- Experience in building AI/ML infrastructure and ML production systems at scale.
- Hands-on experience with Linux, Docker, and other containerization technologies.
- Prior experience at an early-stage startup, developing systems and processes from scratch.
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