Must have

• Python

• SQL

• Airflow

• English

Nice to have

• Java

• Scala

• AWS

• GCP

Requirements description

• Bachelor of Science degree in Computer Science, Information Systems, or related field.
• Data Engineering experience focused on Data Modelling and developing modern data pipelines and applications for reporting and analytics.
• Python, Java or Scala Experience.
• Experience in SQL; can write complex, performant, fail-safe queries.
• Experienced with setting up end to end data pipelines in an enterprise environment.
• Hands-on experience with cloud computing technology like Google Cloud, AWS etc.
• Exposure to Apache Airflow or other data orchestration frameworks.

Offer description

Join RnD in Poland and work for top travel experience company!

Our client, a company that transforms the way people experience travel by providing seamless, innovative, and data-driven solutions for booking tours, activities, and attractions worldwide - is opening an RnD in Poland! With over 200 engineers already on board based in Europe (no USA meetings! :)) and over 20 who joined us in Poland, we are further growing our team. The product has been growing rapidly achieving up to 50% growth in the recent years.

We work hybrid (1 per 2 weeks) from office in Kraków (we can only hire in Poland, for a full time, permanent working contract). You will be hired directly by our client, not via itMatch.

Perks for employees include additional holidays (we don't work in the last week of December!) and bonuses. You could also participate in collaboration weeks, working from various offices based in different countries!

Your responsibilities

1. Implement modern data pipelines for a huge variety of data - from smaller scale B2B data, to terabytes of clickstream traffic.
2. Monitor data pipelines for accuracy, missing data, enhancements, changes, SLA, and billing volumes to ensure all data is captured and processed accurately and when needed.
3. Work with cross-functional stakeholders in defining and documenting requirements for building high quality and impactful data products.
4. Reconcile data issues and alerts between various systems, finding opportunities to innovate and drive improvements.
5. Code, test, and document new or modified data systems to create robust and scalable applications for reporting and data analytics.
6. Contribute to designing optimised data solutions and data products.

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