Senior Data Engineer

Keyrock is looking for a Senior Data Engineer to join its new Central Data Team and build the Keyrock Data Platform. Remote role with hubs in London, Brussels and Singapore; 8+ years of production data systems experience required.

Since 2017 Keyrock has grown into a leading market maker in the digital asset space — 250+ team members, 42 nationalities — with services spanning market making, options trading, high-frequency trading, OTC, and DeFi trading desks. The Central Data Team is a few months old and is building the Keyrock Data Platform to give Keyrockers and the AI agents working alongside them the data and context they need to act fast and autonomously. What You'll Do - Build streaming and batch pipelines that ingest, normalise, and distribute market, trading, and portfolio data, resilient to feed and exchange failures - Build self-serve tooling (SDKs, patterns, templates, AI agents) so other teams publish, consume, and build on data products without waiting on the team - Own data contracts and schema evolution - Design the lakehouse and time-series layer around consumer query patterns - Build and evolve the Data Governance and Data Quality Framework: stale-feed detection, schema validation, range checks, idempotent writes, lineage, ownership, self-healing - Build the derived analytics the business runs on: cross-exchange spreads, VWAP at depth, order book microstructure, portfolio views, exposure, performance - Make observability, cost, and performance first-class from day one - Treat infrastructure as code (Docker, Terraform, CI/CD)

- 8+ years of building production data systems that other people rely on - Strong proficiency in Python and SQL, able to reason about what the engine is doing - Strong understanding of data modelling for both streaming and analytical workloads - Designed and operated streaming systems on Kafka, Redpanda, MSK, or Kinesis, with opinions about partitioning, consumer groups, offsets, and schema registries - Used a time-series store in production (ClickHouse ideally; TimescaleDB, QuestDB, or similar), can talk about table design as a function of query patterns - Worked with a lakehouse architecture; reason about table layout, partitioning, and compaction - Build for self-healing and idempotency; Docker, Terraform, and CI/CD are how you work - Understand financial market data: order books, trades, reference data, portfolios, exposures. Crypto, TradFi, or both a strong plus - Nice to have: Apache Iceberg or Delta Lake; DataHub or similar; Rust

Remote (hubs: London / Brussels / Singapore)