Data Engineer

About the role

Ferryscanner is an online search and booking engine that simplifies ferry ticket purchases worldwide. As the company expands its AI‑driven capabilities, it seeks an experienced Data Engineer to build and scale the data foundations that power analytics, experimentation, automation, and future AI applications.

Key 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 optimise data infrastructure that supports AI and machine learning initiatives.
• Establish best practices around data governance, observability, documentation, lineage, and monitoring.

Required profile

• At least 4 years proven experience as a Data Engineer or similar role, preferably in 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 like Airflow, Dagster or dbt.
• Demonstrated ability to work cross‑functionally with engineering, product, analytics and business stakeholders.

Required skills

• SQL
• Python
• AWS
• GCP
• Azure
• BigQuery
• Snowflake
• Redshift
• Airflow
• Dagster
• dbt
• Vector databases
• Embeddings
• LLM‑powered applications

What we offer

• Opportunity to shape and scale Ferryscanner’s data platform and AI foundations at a pivotal growth stage.
• High‑impact role with direct influence on product development, business decision‑making and AI adoption.
• Collaborative, cross‑functional environment spanning engineering, product, analytics and business teams.
• International team, continuous learning and exposure to cutting‑edge data and AI technologies.
• Hybrid working model and additional annual leave days.