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Senior Data Engineer

Keyrock

Remote
Remote Senior 🇬🇧 English
Data contracts and schema evolution

وصف الوظيفة

About the role

Keyrock’s Central Data Team (CDT) is building a real‑time data platform that powers trading, wealth management, risk, finance and research functions across the company. As a Senior Data Engineer you will be one of the early hires shaping the team’s standards, architecture and culture while delivering high‑performance data solutions for a fast‑moving crypto and DeFi environment.

Key responsibilities

  • Design and implement streaming and batch pipelines that ingest, normalize and distribute market, trading and portfolio data, handling feed and exchange failures.
  • Create self‑serve tooling (SDKs, patterns, templates, AI agents) so other teams can publish, consume and build on data products without bottlenecks.
  • Own data contracts and manage schema evolution to avoid multi‑team coordination overhead.
  • Design the lakehouse and time‑series layer based on consumer query patterns.
  • Develop and maintain a Data Governance and Data Quality framework, including stale‑feed detection, schema validation, range checks, idempotent writes, lineage, ownership and self‑healing mechanisms.
  • Build derived analytics such as cross‑exchange spreads, VWAP at depth, order‑book micro‑structure, portfolio views, exposure and performance metrics.
  • Implement observability, cost monitoring and alerting for all data pipelines.

Required profile

  • Early‑stage team player comfortable contributing to major technical decisions and building foundational work.
  • Strong analytical mindset with the ability to translate business needs into robust data solutions.
  • Experience working in high‑throughput, low‑latency environments, preferably within finance or crypto markets.
  • Self‑motivated, collaborative and able to thrive in a predominantly remote setting.

Required skills

  • Streaming pipeline development
  • Batch pipeline development
  • Data contracts and schema evolution
  • Lakehouse architecture design
  • Time‑series data modeling
  • Data governance and quality frameworks
  • Observability and monitoring of data systems

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