Senior Data Engineer

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.   About the Role As a Senior Data Engineer on our Maritime Models team, you will take technical ownership of the core data pipelines and distributed storage architectures powering Kpler's vessel tracking and predictive analytics. You will design, scale, and optimize high-throughput batch and streaming pipelines that ingest raw maritime telemetry and transform it into actionable intelligence. Working with distributed NoSQL systems like Apache HBase at scale, your work directly ensures the freshness, accuracy, and reliability of the data models our global customers depend on.   Key Responsibilities   Architect, build, and operate high-throughput batch and streaming data pipelines using Java, Python, Apache Kafka, Apache Flink, and Airflow. Design, tune, and maintain scalable distributed NoSQL databases, prioritizing Apache HBase performance for high-volume time-series maritime telemetry. Drive end-to-end operational excellence across containerized cloud environments on AWS using Kubernetes, Docker, Terraform, and GitOps workflows. Define and enforce system reliability metrics, monitoring pipeline latency, data completeness, and uptime to meet strict operational SLAs. Partner with Data Scientists, Product Managers, and SRE teams to translate complex maritime modeling requirements into robust, production-grade infrastructure. Mentor peer engineers, drive code review standards, and leverage modern AI development tools (Copilot, Cursor, Claude) to elevate team productivity.   Experience & Background What you'll need (Must-haves) 5+ years of professional experience as a Data Engineer designing, scaling, and maintaining production-grade data pipelines. Deep, hands-on expertise in distributed NoSQL data stores, specifically Apache HBase, for high-volume time-series storage and query optimization. Strong proficiency in software engineering using Java and Python. Demonstrated experience with stream processing and orchestration tools such as Apache Kafka, Apache Flink, and Apache Airflow. Solid background operating containerized services on AWS cloud environments using Docker, Kubernetes, and Terraform. Nice-to-haves Experience with relational databases such as PostgreSQL. Experience working with geospatial data technologies (e.g., GeoServer) or spatial indexing