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Ferryscanner

Ferryscanner

Data Engineer

Company

Ferryscanner

Role

Data Engineer

Location

Athens, Attikí, Greece

Job type

Full-time

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Salary

Not disclosed by employer

Job description

Ferryscanner is an online search and booking engine that helps people find affordable ferry tickets to exciting destinations around the world. Since 2018, we have been simplifying the ferry booking process by offering ferry tickets for more than 900 destinations across the globe in 25 languages. Our company culture is based on five core values: Ownership, Trust & Respect, Constant Improvement, Customer Obsession, and Being Bold. The Ferryscanner team is made up of people who love travel, thrive in fast-paced environments, and continuously seek practical and innovative solutions.

At Ferryscanner, data is more than reporting, it is a key enabler of how we build products, make decisions, and leverage AI across the business. As we continue to invest in AI-driven capabilities, we are looking for an experienced Data Engineer to build and scale the data foundations that power analytics, experimentation, automation, and future AI applications.

As a Data Engineer, you will own the systems and pipelines that transform raw data into trusted, accessible assets across the organization. You will play a central role in shaping Ferryscanner's data platform, enabling teams to make better decisions and helping Engineering unlock the full potential of AI.

You will report to our Head of Product Development and work closely with our Data Analyst, Product Owners, Software Engineers, and business stakeholders.

Responsibilities

Design, build, and maintain scalable data pipelines, workflows, and data models

Own and evolve Ferryscanner’s data architecture, ensuring reliability, quality, security, and accessibility

Develop and maintain datasets, warehouses, and reporting foundations that support analytics and decision-making

Partner with the Data Analyst to improve data availability, self-service analytics, and reporting capabilities

Build and optimize data infrastructure that supports AI and machine learning initiatives

Establish best practices around data governance, observability, documentation, lineage, and monitoring

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