Senior Data Engineer

• 8+ years of building production data systems that other people rely on
• Strong proficiency in Python and SQL, including reasoning about what the engine is doing with a query
• Code that's easy for someone else to read, test, and delete later
• Strong understanding of data modelling for both streaming and analytical workloads
• Efficiency, quality, idempotency, and observability taken seriously by default
• 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) and can talk about table design as a function of query patterns
• Worked with a lakehouse architecture and reason about table layout, partitioning, and compaction as design choices that shape query performance and storage cost
• Build for self-healing and idempotency: reprocessing is safe, retries don't double-write, and the system recovers without a human in the loop
• Docker, Terraform, and CI/CD are how you work, not a separate DevOps thing
• Think about cost and performance early
• Instrument as you build: logs, metrics, and traces are part of the system from day one
• Design for data quality and governance up front covering contracts, validation, lineage, and ownership
• Reason from first principles when a problem is new, stay pragmatic when it isn't, and update your view when you learn more
• Treat the trading desks, wealth and asset management, product, risk, finance, compliance, and research as customers of what you build, and communicate with them that way
• Optimise for outcomes over output
• Take ownership end-to-end: design, ship, operate, improve
• Say what you think including when it's an unpopular take, and change your mind when the argument is better
• Make the people around you better: reviews are real, juniors grow from working with you, and peers want to work with you again
• Curious about how markets work
• Honest about what you know and what you don't, and quick to close the gap
• Understand financial market data: order books, trades, reference data, portfolios, exposures

Central Data Team (CDT)

Only a few months old; building the Keyrock Data Platform to give Keyrockers and AI agents the data and context they need to act fast and autonomously.

• A from-scratch mandate: among the first hires in CDT, shaping the platform, standards, and team culture
• Strong partners: close working relationships with Architecture, Infrastructure, Platform, and the desks
• Autonomy on how you work: flexible hours, remote-first, business-hours on-call shared across the team
• A competitive salary package, with various benefits
• Regular online get-togethers and a yearly onsite where everyone's in the same room