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.

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.

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.

Big Data Engineer

Key skills