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
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-havesExperience 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).
