Senior Agentic Database Systems Engineer (C/C++) – KeewanoDB

We are developing KeewanoDB — the first real-time database built for AI agents at scale. Our system is designed around time-series and unstructured data, using a custom storage format optimized for append-heavy, real-time workloads. We leverage vectorized execution, SIMD, and cache-efficient memory layouts to achieve extreme low-latency, high-throughput performance, pushing modern CPUs (L1/L2/L3) to their limits.

• Design and implement core components of the database engine (query engine, execution engine, storage engine)
• Build vectorized execution pipelines optimized for SIMD
• Design and evolve a time-series optimized storage engine (custom on-disk + in-memory formats)
• Work on unstructured / event-driven data representations and efficient indexing/querying strategies
• Own memory layout, compression, and data defragmentation
• Develop cache-aware / cache-efficient data structures with deep understanding of CPU cache behavior
• Implement distributed data primitives: sharding, partitioning, replication, and data locality
• Profile and optimize performance at the CPU level (cache misses, branch prediction, memory bandwidth)

• 7+ years of experience in high-performance systems programming (C/C++)
• Deep understanding of computer architecture (CPU pipelines, cache hierarchies, memory access patterns)
• Strong experience with low-level optimization and profiling tools
• Proven knowledge of multithreaded development (lock and lockfree)
• Expertise in algorithms and data structures, especially cache-aware designs
• Experience in one or more of the following:
• Database internals (query engines, storage engines, query planners/optimizers)
• Time-series or real-time data systems
• High-performance systems (trading systems, game engines, networking stacks, compilers)
• Distributed systems (sharding, partitioning, consistency models)

• Experience with vectorized execution engines (e.g., DuckDB-style processing)
• Experience designing custom storage formats or low-level data layouts
• Experience handling unstructured or semi-structured data at scale
• Background in query optimization and execution planning

• Build a new class of database optimized for AI agents and real-time decision-making
• Work on deep systems problems across storage, execution, and hardware efficiency
• Own critical parts of a database engine from an early stage