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 700 experts from 35+ 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.
We are looking for a Data Engineer to join our Maritime Ingress team, where you’ll build systems that power global maritime intelligence by processing massive volumes of real-time streaming data and delivering mission-critical insights used worldwide.
In this role, you will design and operate large-scale distributed systems that ingest and process continuously flowing data from diverse external sources and formats. You will tackle complex global data ingestion challenges while ensuring accuracy, reliability, and performance in platforms that support trusted, real-time maritime intelligence.
If you enjoy solving complex data challenges, building resilient large-scale systems, and working with real-time streaming architectures in a high-growth environment, this is an opportunity to make a meaningful global impact. Your work will directly contribute to delivering accurate and reliable maritime intelligence used by customers worldwide.
Your mission is to:
Build and operate scalable batch and streaming data pipelines (e.g., Kafka, Spark), ensuring reliable ingestion, processing, and recovery in production environments.
Design and implement data quality and governance mechanisms including validation, lineage, and monitoring to ensure trusted and accurate datasets.
Develop observability and reliability tooling across data platforms using metrics, logs, traces, CI/CD, automated testing, alerting, and incident response.
Contribute to robust and scalable data architecture, applying software engineering best practices, reusable components, and clean design principles.
Collaborate with Product, Engineering, Analytics, and domain experts to translate requirements into scalable data solutions with measurable impact.
Participate in Agile delivery processes, including planning, estimation, reviews, and retrospectives, contributing to predictable and iterative delivery.
Continuously optimize performance, scalability, and cost efficiency across data pipelines, storage, and infrastructure.
Ensure security, privacy, and compliance standards are embedded in all data handling and processing workflows.
Data-focused software engineering background with production experience with stateful, large‑scale data systems.
Experience with streaming technologies such as Apache Kafka, Kafka Streams, and Apache Flink, as well as Avro and stateful data processing.
Programming experience in Java, Scala, Python, or similar (language flexible)
Strong understanding of distributed, event‑driven architectures
Strong debugging and performance optimization skills in data-intensive systems
Ownership, accountability, and a proactive, execution-focused mindset with strong pragmatic problem-solving.
Cross-functional collaboration with clear, structured communication, especially in ambiguous or fast-moving environments.
Desirable:
Data‑pipeline orchestration and workflow management
Cloud infrastructure & infrastructure‑as‑code
Containerized microservices environments
JVM build‑system tuning (Gradle/Maven)
Based on 1,433 disclosed Data & ML salaries on RoleSuite, the role pays a median of $162K/year, with most offers between $127K and $203K (10th–90th percentile: $105K–$245K).
See the full Data & ML salary breakdown →