Database Systems Engineer Hybrid Engineering

We’re looking for a Database Systems Engineer who thrives at the frontier of distributed databases, storage engines, transactional systems, and analytical query execution.

FastS3 is building AI-native data infrastructure from the ground up. We are looking for an engineer to help design the database layer that sits above PixelDB, our distributed key/value object storage engine, and works with TEMPANO, our custom Iceberg catalog and compaction API.

This role is not about building a conventional application database, a Postgres extension, or a simple lakehouse connector. It is about helping design the foundation for a system that can support transactional, analytical, and vector workloads over the same live data layer.

You’ll work closely with engineering leadership to prototype and build the core components of a distributed table engine: commit logs, MVCC, stable Row IDs, secondary indexes, snapshot publication, compaction, query execution, and integration with Iceberg-style table metadata.

• Design and prototype a transactional table engine on top of PixelDB.
• Work on MVCC, commit logs, transaction visibility, snapshot isolation, and serializable consistency.
• Design stable Row  so indexes point to logical rows rather than physical storage locations.
• Build metadata and manifest flows that connect live transactional writes to TEMPANO and Iceberg-compatible analytical snapshots.
• Help design compaction, version cleanup, and garbage collection that do not block long-running analytical queries.
• Explore query execution paths for point reads, range scans, analytical scans, joins, and vector retrieval.
• Work on distributed storage layout, partitioning, object placement, erasure‑coded data, and recovery semantics.
• Collaborate with systems and AI engineers to support agentic workloads that require transactional, analytical, and semantic access to live data.
• Benchmark latency, throughput, consistency behavior, write amplification, and query performance under concurrent workloads.

• Strong systems programming experience in Rust, C++, Go, or
  
  C.
• Practical knowledge of database internals, storage engines, distributed databases, or query engines.
• Experience with MVCC, WAL/commit logs, transaction processing, indexing, compaction, or recovery.
• Understanding of distributed systems concepts such as consensus, replication, failure recovery, consistency, and concurrency control.
• Familiarity with analytical formats or engines such as Apache Iceberg, Delta Lake, Parquet, Arrow, Trino, Spark, DuckDB, or Click House.
• Ability to reason deeply about tradeoffs between OLTP, OLAP, and vector workloads.
• Comfort moving from architectural design to prototype code, benchmarks, and production‑quality implementation.
• Strong debugging, profiling, and performance‑analysis skills.

• Experience building or contributing to database kernels, storage engines, query planners, transaction managers, or distributed SQL systems.
• Hands‑on work with PostgreSQL internals, Cockroach DB, YugabyteDB, TiDB, Foundation DB, ScyllaDB, Cassandra, Click House, DuckDB, Neon, or similar systems.
• Experience with Apache Iceberg, Delta Lake, table catalogs, manifest generation, compaction, or lakehouse metadata.
• Knowledge of vector indexes such as HNSW, IVF, PQ, ANN search, or hybrid vector/SQL execution.
• Experience designing systems around stable logical IDs, append‑only storage, object storage, LSM trees, B‑trees, or log‑structured architectures.
• Background in high‑performance networking, distributed storage, RDMA, erasure coding, or object storage.
• Open‑source contributions, papers, or deep technical writing related to databases,…