Data Engineer

Fleet Management Limited

Our 30-year journey rides on the passion of over 27,000 seafarers and 1,000 onshore professionals. Today, we are one of the largest independent third-party ship management companies managing over 650+ diverse types of vessels.

Headquartered in Hong Kong SAR, China, we operate on a global scale having 27 offices in 12 countries. Our client base spans over 100 world-class ship owners, including Fortune 500 companies from China, Greece, India, Japan, Korea, Netherlands, Norway, Turkey and the USA, among others.

In a shore career at Fleet, you will be working with a team of a highly passionate, self-driven and committed group of people. We aim to be a place where you can achieve your full potential, regardless of your background.

We are looking for individuals who are ambitious about making a strong contribution to Fleet&aposs short and long-term sustainable growth whether you are dealing directly with clients or working in a role supporting the business, such as technology, legal or communications.

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 operationsno 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 hiccupsthen 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 craftno 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