Role overview
Description
As part of AI Hub within Data Insights & AI Department (DIA), you will lead Hapag-Lloyd's AI transformation initiatives and AI adoption. Contributing to AI implementation across our organization, you will create new business value with AI technology for shipping and logistics applications for our 17,000+ colleagues.
In short: you will design, build, and operate the end-to-end data and ML/AI infrastructure and pipelines so that high-quality data reliably powers model development, deployment, monitoring, and continuous improvement.
CorporateDescription
With a fleet of 313 modern container ships and a total transport capacity of 2.5 million TEU, Hapag-Lloyd is one of the world's leading liner shipping companies. In the Liner Shipping segment, the Company has around 14,000 employees and 400 offices in 140 countries. Hapag-Lloyd has a container capacity of 3.7 million TEU – including one of the largest and most modern fleets of reefer containers. A total of 133 liner services worldwide ensure fast and reliable connections between more than 600 ports on all the continents. In the Terminal & Infrastructure segment, Hapag-Lloyd has equity stakes in 21 terminals in Europe, Latin America, the United States, India and North Africa. Around 3,000 employees are assigned to the Terminal & Infrastructure segment and provide complementary logistics services at selected locations in addition to the terminal activities.
OrganizationDescription
What you'll work on
- Strong programming skills in Python (clean and testable code; packaging; CI/CD)
- Solid experience with data engineering (building and maintaining ETL/ELT pipelines and batch/stream processing)
- Hands-on with Databricks (Spark, Delta Lake, workflows/jobs) and performance tuning in distributed environments
- Proficiency in SQL (data modeling, query optimization, complex transformations)
- Hands-on experience with AWS data & AI services (e.g. S3, Glue, Lambda, Step Functions, ECR, RDS)
- Comfort in building the API endpoints (Databricks API endpoints, VPC endpoints) and working with event-driven architectures (Kafka)
- Experience integrating with ML/AI systems (LLM/RAG pipelines, vector databases/embeddings, model serving) is a plus
- Strong analytical and problem-solving skills (ability to debug data issues end-to-end)
- Good communication skills (efficient collaboration with data scientists, ML engineers, and business stakeholders)
- Very good English
- Build and maintain complex data management backend systems that combine core data sources into data warehouses or other accessible structures Perform data conversions, imports and exports of data within and between internal and* software systems
- Develop and manage data pipelines consisting of a series of stages through which data flows optimally
- Drive automation through effective metadata management
- Learning and using modern data preparation, integration and AI-enabled metadata management tools and techniques
- Design and implement processes to ensure data integrity and standardization
- Recommend and implement data reliability, efficiency, and quality improvements
- Ensure the collected data is within required quality standards
- Work with your teammates to implement governance for role-based access control (RBAC), cost containment and cloud provider account management
What we're looking for
- Private medical care (Medicover)
- Gym card (Multisport)
- Attractive annual bonus up to 22,5% (depending on company performance results)
- Group life insurance and employee capital plan (PPK)
- Cafeteria benefit system (cinema tickets, vouchers etc.)
- Focus on healthy lifestyle (fruit days, bike competitions, football training)
- Charity and volunteer initiatives
- Modern and well-connected office (Alchemia complex in Gdańsk Oliwa)
- Relocation support (financial support, covering immigration process and polish-language lessons for non-Polish citizens)
- Internal learning management system
- Development budget (sharing the costs of certifications and conferences/ IT events)
- Flexible working hours and home office possibility (hybrid work model)