Senior Software Engineer — Distributed Data and Retrieval Platform

Bloomberg runs on financial data. Every price corporate action benchmark security master update point-in-time view and derived fact must be correct fast durable and available at global scale.

Our team builds the distributed OLTP storage layer behind some of Bloombergs most critical financial data systems. We provide deterministic purpose-built APIs that let Bloomberg engineers read and write financial data safely while our platform absorbs the complexity of Vitess MySQL MyRocks RocksDB sharding replication compaction observability capacity management and failure recovery.

This is a role for engineers who want to work close to the metal of distributed data systems. You will reason about query routing storage-engine behavior schema evolution read-your-own-write consistency hot shards p99 latency saturation compaction debt replication lag backfills and safe production rollout.

When Vitess MySQL MyRocks RocksDB or related tooling need to change to meet Bloombergs requirements we are prepared to make those changes and contribute them upstream.

Our Team

We build the store services that make Bloomberg financial data discoverable queryable point-in-time accurate and reliable across the company.

Our systems expose structured schema-evolving datasets through unified APIs while preserving consistency low latency operational safety and auditability. They serve both latency-sensitive OLTP traffic and high-throughput readers that power downstream systems including Bloombergs S3-based BBDS lakehouse.

This role is not about Spark internals. It is about making a distributed OLTP storage platform remain correct and performant under very different workload shapes: small deterministic reads write-heavy dataflows backfills table materialization snapshot reads and massively parallel batch access.

We care deeply about production behavior. That means source-level debugging disciplined benchmarking workload modeling high-cardinality observability automated operations safe rollout and tooling that helps engineers understand exactly why the system behaved the way it did.

Whats in it for you

• Build OLTP store services supporting billions of mutations and transactions across thousands of shards.

• Work deeply with Vitess MySQL MyRocks RocksDB and the operational realities of running them at scale.

• Design deterministic low-latency access patterns instead of relying on arbitrary SQL execution.

• Build systems for time series bi-temporal data point-in-time queries versioned facts snapshot reads and derived table materialization.

• Diagnose and improve performance across the stack: application code JVM Linux networking MySQL MyRocks RocksDB and Vitess.

• Build observability for fast production debugging using structured events traces high-cardinality dimensions SLIs and storage-engine telemetry.

• Make store services robust under both client-facing OLTP traffic and highly parallel batch-reader workloads.

• Influence the architecture of Bloombergs financial data platform.

• Contribute improvements back to upstream open-source systems when Bloombergs requirements push beyond what exists today.

Youll need to have

• 4 years of professional software engineering experience.

• Strong programming skills in one or more of Java Go C or C.

• Deep experience or strong interest in database internals distributed systems storage engines or large-scale data infrastructure.

• Strong debugging skills across application code infrastructure networking and Linux.

• Experience reasoning from first principles about throughput latency contention queuing caching compaction replication saturation and failure modes.

• A degree in Computer Science Engineering Mathematics or equivalent practical experience.

Wed love to see

• Contributions to infrastructure projects such as Vitess MySQL RocksDB MyRocks or similar systems.

• Experience operating or extending sharded MySQL Vitess distributed SQL or large-scale OLTP platforms.

• Experience profiling and tuning production systems with JFR async-profiler perf eBPF flame graphs heap analysis or custom benchmarking frameworks.

• A track record of building reliable systems where correctness latency and operability matter.

• A passion for mentoring engineers and raising the technical bar of a platform team.