Database Systems Engineer Madrid-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.

Se pueden requerir diversas habilidades interpersonales y experiencia para el siguiente puesto. Por favor, asegúrese de consultar la descripción a continuación con atención.
Database Systems Engineer — Distributed Storage & Transactional Query Engines
Role Summary

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.

What You’ll Be Doing

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• 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 ID abstractions 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.
  

What We Need to See

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• 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 ClickHouse.
  
• 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.
  

Ways to Stand Out

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• Experience building or contributing to database kernels, storage engines, query planners, transaction managers, or distributed SQL systems.
  
• Hands‑on work with PostgreSQL internals, CockroachDB, YugabyteDB, TiDB, FoundationDB, ScyllaDB, Cassandra, ClickHouse, 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, storage engines, or distributed systems.
  

Why Join Us?

FastS3 is building infrastructure for the next generation of AI‑native data systems. Our vision is to move beyond fragmented pipelines, replicated warehouses, and bolt‑on vector extensions by designing a data layer where transactional, analytical, and semantic workloads can operate over the same live data.

You’ll join a team working at the intersection of distributed storage, database internals, lakehouse architecture, vector retrieval, and AI infrastructure. If you’re excited by the idea of building a new database architecture instead of extending yesterday’s systems, this is the role.

We’re growing our team in Madrid and looking for engineers who want to build core infrastructure from first principles. xqbhyrx

FastS3 is proud to be an inclusive, equal opportunity employer committed to diversity, equity, and accessibility for all.

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Hay opciones de teletrabajo/trabajo desde casa disponibles para este puesto.