Data Engineer

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.

• →

  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

• At least 4 years proven experience as a Data Engineer, Analytics Engineer, Data Platform Engineer, or similar role, ideally within a product-led, marketplace, travel, or consumer technology business

• Strong SQL and Python skills, with experience building production-grade data pipelines and scalable data models

• Experience with cloud platforms (AWS, GCP, or Azure) and modern data warehouses such as BigQuery, Snowflake, or Redshift

• Hands-on experience with data orchestration and transformation tools such as Airflow, Dagster, dbt, or equivalent

• Strong understanding of data architecture, ETL/ELT processes, data governance, and software engineering best practices

• Experience building reliable monitoring, observability, and quality assurance processes for data systems

• Demonstrated ability to work cross-functionally with Engineering, Product, Analytics, and business stakeholders

• Familiarity with AI and machine learning workflows, including data pipelines that support model training, evaluation, and deployment

• Experience with vector databases, embeddings, retrieval systems, or LLM-powered applications is a strong plus

• Comfortable leveraging AI-assisted development tools and modern engineering workflows

• Strong analytical, problem-solving, and organizational skills, with the ability to balance long-term platform thinking with practical execution

• Ownership mentality, takes end-to-end accountability and drives initiatives forward

• Strategic thinking with executional bias, balances long-term architecture with practical delivery

• Cross-functional collaboration, builds trusted relationships across technical and business teams

• Systems thinking, designs scalable, reliable, and maintainable solutions

• Data-driven mindset, uses measurement and evidence to inform decisions and improve outcomes

• Curiosity and innovation mindset, actively explores emerging data and AI technologies

• Adaptability and growth mindset, thrives in a fast-moving environment and embraces continuous learning

• Bias for simplicity, values clarity, reliability, and ease of use in both systems and processes