Staff Engineer - Data platform

3 нед. назад

United KingdomEuropeLeadHybrid

flinkicebergdata architecturedistributed systemsreal-time pipelines

Lead engineering role responsible for overseeing data platform architecture and operational maturity of pipelines.

At , we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors.   Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 850 experts from 69 countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success.   The Data platform tribe builds the tooling and foundational datasets that underpin the rest of Kplers p

Design, develop and maintain real-time and batch data pipelines.
Improve reliability and observability, taking ownership of the operational excellence of the data pipelines
Own complex cloud-based infrastructure (You build it, you run it.)
Manage data products with a diverse set of stakeholders.
Provide strategic and technical leadership, supporting and mentoring other engineers in the team.
Gain a deep understanding of a complex domain with many cross-team dependencies.
Own features from end to end.

10+ years of experience in Data engineering or Software engineering positions (ideally with DevOps exposure).
Strong technical foundations in backend, data or distributed systems, with significant experience using Java.
Proven experience with streaming technologies either Kafka or Flink.
Ability to work effectively with Product, engineering and domain stakeholders to translate business priorities into clear technical and delivery outcomes.
Deep understanding of distributed systems and architectural trade-offs.
Significant professional use of AI agents
Experience working in a cloud environment (AWS, GCP, Azure)
Strong command of written and spoken English.
Proven experience of batch technologies: Spark.
Exposure to Infrastructure as code (Terraform, Pulumi)
Professional experience using Python or Scala.