Big Data Engineer

You will:

• Design and implement data processing systems, including data warehouses, data lakes, and real-time processing platforms;
• Configure and manage technologies such as Hadoop, Spark, and Kafka, as well as cloud environments across Azure, AWS, and GCP;
• Build and maintain automated ETL/ELT processes for data collection, cleansing, and transformation;
• Ensure seamless, reliable data flow between diverse systems and sources, with a strong focus on data quality and consistency;
• Optimize data systems for high-volume, high-velocity workloads;
• Design and implement distributed computing solutions that maintain performance at scale, proactively identifying and resolving bottlenecks.

Requirements:

• Hands-on experience with PySpark for large-scale data processing;
• Strong knowledge of Apache Kafka for real-time data streaming;
• Cloud platform experience across Azure, AWS, and/or GCP;
• Proven ability to design and optimize ETL/ELT pipelines;
• Familiarity with Hadoop ecosystems and distributed computing principles;
• Solid understanding of data warehouse and data lake architectures;
• Nice to haves: experience with infrastructure-as-code tools (Terraform, Bicep), knowledge of data governance and security best practices, exposure to orchestration tools such as Apache Airflow or Azure Data Factory.

Benefits:

• Flexible Working Hours – Manage your workday with flexibility and the option to work from home when needed, while enjoying our city-centre office as a convenient, collaborative workspace;
• Culture & Connection – From team bonding activities like Christmas parties and summer events to spontaneous celebrations, monthly breakfasts, or team lunches, we celebrate wins—big or small—together;
• Competitive salary.