Staff Engineer - Data platform

Job description

At Kpler, 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 products. They drive the development of large-scale, distributed, real-time pipelines for geospatial data using tools like Flink and Iceberg. As a Staff engineer you will be part of the engineering leadership within Kpler, responsible for aligning priorities and ensuring the delivery of key initiatives across team and department boundaries. You will be responsible for raising the bar on the operational maturity of our pipelines while overseeing and defining Kpler’s data architecture. Your mission is to 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. You could be a good fit if you have 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 Experi…