Senior Data Engineer

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. ## About the Role ## As a Senior Data Engineer in our Maritime & Logistics division, you will take technical ownership of the data pipelines and processing infrastructure that power real-time global trade intelligence. You will architect, build, and maintain high-throughput batch and streaming pipelines that ingest complex maritime feeds from global providers. Working closely with Product, SRE, and Data Science teams, your work will directly ensure the accuracy, freshness, and scalability of the data that underpins our flagship analytics platforms. ### Key Responsibilities ## - Architect, build, and optimize scalable data pipelines and stream-processing workflows using Python, Apache Spark, Kafka, and Airflow. - Maintain and evolve data architecture across polyglot backend microservices (Python/FastAPI, PHP, Java/Scala) to support continuous analytics delivery. - Ensure end-to-end data quality, freshness, and reliability by implementing automated validation, monitoring frameworks, and strict SLA tracking. - Provision and manage cloud-native data infrastructure on AWS using Terraform, Docker, and automated CI/CD practices. - Collaborate cross-functionally with Product Managers, SRE, Platform Engineers, and Data Scientists to translate complex domain needs into performant data products. - Drive operational and cost efficiency across data infrastructure, optimizing cloud resource usage and pipeline performance. ### Experience & Background ### What you'll need (Must-haves) - 6+ years of professional software or data engineering experience building complex, production-grade data processing systems. - Strong expertise in Python and hands-on experience with distributed data processing tools such as Apache Spark, Kafka, or Airflow. - Advanced knowledge of database design, SQL performance tuning, and data modeling across relational and NoSQL stores. - Proven experience managing cloud infrastructure on AWS, utilizing Terraform, Docker, and modern CI/CD pipelines. - Practical experience writing clean, maintainable code in polyglot environments (Python, Java/Scala, or PHP). ### Nice-to-haves - Experience building RESTful APIs using Python frameworks like FastAPI. - Background handling high-throughput time-series, geospatial, or maritime logistics datasets. - Exposure to cloud FinOps, observability, and cost management tooling (e.g., Datadog, Astronomer).