Data Engineer: Azure, Databricks & Spark Expert

Inetum Polska; Warszawa; On-site; PLN 150,000 - 220,000; Full time

Job summary

Inetum Polska is seeking an experienced Data Engineer to design, develop, and maintain scalable data platforms and pipelines within the Azure ecosystem. The role focuses on building robust data processing workflows using modern technologies and collaborating with analysts and architects to deliver business value.

You will work with Databricks and Apache Spark to handle large data volumes, ensure data quality and security, and apply best practices in Big Data processing.

Qualifications

• Proven experience as a Data Engineer.
• Hands-on experience with Databricks.
• Experience with Azure Data Processing and Storage Services.
• Practical experience with Apache Spark.
• Strong knowledge of SQL.
• Good understanding of Big Data processing standards, tools, and best practices.
• Proficiency in Polish language.
• English language skills at B2 level or higher.

Responsibilities

• Design, develop, and maintain scalable data solutions and data pipelines.
• Build and optimize data processing workflows.
• Work with Databricks and Apache Spark to process large volumes of data.
• Develop and maintain data solutions within the Microsoft Azure ecosystem.
• Collaborate with data analysts, architects, and other stakeholders to deliver business value.
• Ensure data quality, performance, and security across data platforms.

Skills

SQL proficiency

Big Data practices

Polish language

English level

Education

Bachelor's or Master's in CS/Data Eng

Tools

Databricks

Apache Spark

Azure Data Services

Job description

Inetum Polska is seeking an experienced Data Engineer to design, develop, and maintain scalable data platforms and pipelines within the Azure ecosystem. The role focuses on building robust data processing workflows using modern technologies and collaborating with analysts and architects to deliver business value.

You will work with Databricks and Apache Spark to handle large data volumes, ensure data quality and security, and apply best practices in Big Data processing.