Big Data Engineer with Spark/Scala (f/m/x)
⚲ Warszawa
25 200 - 29 400 PLN netto (B2B)
Wymagania
- Apache Spark
- SQL
- Scala
- Java
Opis stanowiska
Join a team building a modern data platform that supports key business and analytical processes in an international environment. We are looking for experienced data engineers who want to work with Big Data technologies, develop scalable solutions, and have a real impact on the architecture and development of the platform.
Your tasks
• Design, develop, and maintain scalable data pipelines and processing systems
• Build efficient solutions for collecting, transforming, and integrating data from diverse sources
• Translate business and analytical needs into technical requirements and data models
• Optimize data workflows to ensure high performance, reliability, and resilience
• Develop and maintain Spark/Scala-based jobs for data ingestion and transformation
• Participate in code reviews and contribute to continuous improvement initiatives
• Work collaboratively within an Agile (Scrum) environment, delivering business value in short iterations
• Follow best engineering practices such as TDD, BDD, clean coding, and DevOps principles
• Create and maintain technical documentation and test plans
Requirements
• Minimum 5 years of experience in Big Data Engineering
• Practical knowledge of Spark, SQL, and Java or Scala
• Experience with additional technologies: Python, Airflow, Git, CI/CD, APIs, microservices, DevOps
• Analytical thinking and problem-solving skills
• Independence, proactive attitude, and good organization of work
• Ability to collaborate in an international environment
• Very good command of Polish and English
• Willingness to work in a hybrid model (2–3 days per week in the office)
• Residence in Poland
Nice-to-have requirements
• Banking sector experience
Your tasks
• Design, develop, and maintain scalable data pipelines and processing systems
• Build efficient solutions for collecting, transforming, and integrating data from diverse sources
• Translate business and analytical needs into technical requirements and data models
• Optimize data workflows to ensure high performance, reliability, and resilience
• Develop and maintain Spark/Scala-based jobs for data ingestion and transformation
• Participate in code reviews and contribute to continuous improvement initiatives
• Work collaboratively within an Agile (Scrum) environment, delivering business value in short iterations
• Follow best engineering practices such as TDD, BDD, clean coding, and DevOps principles
• Create and maintain technical documentation and test plans
Requirements
• Minimum 5 years of experience in Big Data Engineering
• Practical knowledge of Spark, SQL, and Java or Scala
• Experience with additional technologies: Python, Airflow, Git, CI/CD, APIs, microservices, DevOps
• Analytical thinking and problem-solving skills
• Independence, proactive attitude, and good organization of work
• Ability to collaborate in an international environment
• Very good command of Polish and English
• Willingness to work in a hybrid model (2–3 days per week in the office)
• Residence in Poland
Nice-to-have requirements
• Banking sector experience
🔍 Dekoder Ogłoszenia
🔴
have a real impact on the architecture and development of the platform
Twoje pomysły mogą być brane pod uwagę, ale ostateczne decyzje podejmuje ktoś inny.
🟡
Translate business and analytical needs into technical requirements and data models
Będziesz musiał często rozmawiać z osobami nietechnicznymi i tłumaczyć ich wymagania na język techniczny.
🟡
Independence, proactive attitude, and good organization of work
Oczekuje się, że będziesz samodzielnie zarządzać swoim czasem i zadaniami bez ciągłego nadzoru.
🔴
Work collaboratively within an Agile (Scrum) environment, delivering business value in short iterations
Przygotuj się na częste zmiany priorytetów i presję dostarczania działających rozwiązań w krótkich cyklach.
🔴
Follow best engineering practices such as TDD, BDD, clean coding, and DevOps principles
Oczekuje się, że będziesz stosować te praktyki, nawet jeśli nie ma na to wystarczająco dużo czasu lub zasobów.