Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in C or C .
- 2 years of experience designing, building, or maintaining backend infrastructure, distributed systems, or large-scale storage architecture.
- Experience developing concurrent, multithreaded systems, or engineering core database internals (e.g., storage engines, transaction processing, indexing).
- Experience building fault-tolerant distributed systems, data replication mechanisms, or consensus protocols (e.g., Paxos, Raft).
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- Experience modifying or contributing to the internals of open-source databases (e.g., PostgreSQL, MySQL) or storage engines (e.g., RocksDB, LevelDB).
- Experience with query processing, cost-based query optimization, compiler theory, or vectorized execution engines.
- Experience engineering lock-free data structures, latches, and advanced thread synchronization mechanisms.
- Experience analyzing and tuning system performance using standard benchmarks (e.g., TPC-C, TPC-H) and low-level profiling tools (e.g., perf, eBPF).
About the jobIn this role, you will work on the core database engine. You will design and implement features deep within the stack-ranging from the Query Optimizer and Execution Engine to the Storage Layer and Transaction Manager. You will collaborate with engineering leadership to push the boundaries of what a relational database can do in the cloud.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Responsibilities - Design and implement high-performance C components for the AlloyDB kernel, focusing on critical areas such as storage engines, indexing, buffer management, and transaction logging.
- Build and harden distributed consensus and replication mechanisms to ensure data consistency, high availability, and zero data loss during failovers across large-scale deployments.
- Integrate upstream PostgreSQL features into a custom, cloud-native storage engine, ensuring full SQL compliance while maximizing the advantages of Google's underlying hardware and infrastructure.
- Profile and optimize the core database path to reduce latency, improve concurrency, and optimize query execution plans for demanding, enterprise-scale workloads.
- Collaborate with cross-functional teams to resolve architectural challenges and performance bottlenecks across a fleet of databases.
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