Data Engineer

Fleet Management Limited

Fleet Management Limited (Fleet), headquartered in Hong Kong SAR, China, is one of the world's leading third-party ship management companies, overseeing a diverse portfolio of over 600 vessels across all major shipping segments, and newbuilding supervision. With a legacy spanning more than 30 years, Fleet has built its reputation on three enduring principles: people, partnership, and performance.

At the heart of Fleet's operations is a deep commitment to human capital. The company employs over 27,000 seafarers and 1,300 shore-based professionals worldwide, united by a shared mission to deliver safe, efficient, and sustainable maritime operations.

Our client base spans world-class ship owners, including Fortune 500 companies from China, Greece, India, Japan, Korea, Netherlands, Norway, Turkey and the USA, among others. With 27 offices across 12 countries, Fleet Management ensures comprehensive coverage and support for shipowners, operators, and seafarers worldwide.

Visit our website: www.fleetship.com

A shore career with Fleet promises you the opportunity to work with a team of highly passionate, and self-driven professionals, enabling you to enhance your skills, while continuously learning.

As a Data Engineer, your typical day will cover the following areas:

1. Build & Optimise Data Systems

(Focus: Core engineering work with a balance of pipelines and infrastructure)

• Create & maintain data highways: Develop and manage cloud-based data lakes, warehouses, and ETL/ELT pipelines that ingest, process, and deliver data from 630+ ships and external sources

• Keep systems shipshape: Monitor cloud infrastructure performance, resolve bottlenecks, and ensure scalability/reliability for 24/7 maritime operations—no room for "set and forget."

• Secure the cargo: Implement data quality checks, encryption, and compliance standards (GDPR, SOC2) to protect sensitive maritime telemetry and operational data

• Automate the mundane: Use tools like Airflow to streamline workflows and reduce manual intervention in pipeline maintenance

2. Support Analytics & Troubleshoot Issues

(Focus: Enabling insights while keeping systems running smoothly)

• Fuel AI/ML engines: Partner with Data Scientists to prep datasets for predictive models (e.g., fuel efficiency, preventative maintenance, etc.) and troubleshoot pipeline issues impacting their work

• Solve data mysteries: Diagnose root causes of pipeline failures, data discrepancies, or MLOps hiccups—then implement fixes that prevent repeat headaches

• Map the data terrain: Document source-to-target mappings, conduct data profiling, and clarify dependencies so analysts can self-serve without guesswork

• Stay curious: Experiment with new tools and techniques to improve data quality, system performance and pipeline resilience

3. Collaborate & Learn

(Focus: Teamwork and growth in a fast-paced environment)

• Be the glue: Work closely with onshore and offshore developers, IT Operations, and stakeholders across Fleet to deliver solutions that balance technical rigor with real-world usability

• Communicate clearly: Break down complex data concepts for non-technical audiences (think ship captains, not just engineers) and ask questions to avoid ambiguity

• Learn by doing: Shadow senior engineers, participate in code reviews, and absorb best practices to level up your craft—no prior maritime experience required, but curiosity is a must.

Essential :

• 3+ years' experience in a Data Engineering role using SQL, PySpark and AirFlow

• Strong understanding in Data Lake and Data Warehouse design best practices and principles

• Practical hands-on experience in cloud-based data services for ETL/ELT covering AWS EC2, S3 Storage and EMR

• Ability to manage and enhance infrastructure related to environments covering Spark, Hive, Presto

• Experience with databases such as Postgres, MySQL, Oracle

• Strong work ethic and ability to work independently on agreed goals

• Clear communication skills in English – both in speaking and writing

Desirable :

• Deployment experience and management of MLOps framework, such as AWS SageMaker AI, ECR

• Experience in other cloud platform and hybrid cloud infrastructure, e.g. GCP, Azure

• Experience in the maritime industry