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Data Engineer - Azure Data Marketplace Platform

Job Description - Data Engineer - Azure Data Marketplace Platform



Data Engineer - Azure Data Marketplace Platform 




Location: Warsaw  




Contract Type: 3 Months 
Job type: Hybrid 






  • Work on cutting-edge AI data marketplace technology at Manager level 








  • Build world-class data products using Azure Data Factory and Databricks 








  • Join cross-functional teams focused on stability, optimisation and innovation 








  • Leverage 8+ years of experience in a role that values resilience and scalability 






Company Overview 




Our client is a global professional services organisation operating at the forefront of data engineering and AI technology. They are building an advanced data marketplace platform designed to deliver robust, scalable data assets to consumers across the business. With a focus on innovation, collaboration and technical excellence, they provide an environment where experienced engineers can contribute to meaningful projects that shape the future of data-driven decision making. 




They are seeking an experienced Data Engineer at Manager level to join their team in Warsaw. This contract role offers the opportunity to work on sophisticated data ingestion pipelines within their AI Data Marketplace, collaborating with talented professionals to build resilient, fault-tolerant systems. 




Position Overview 




As a Data Engineer, you will play a central role in developing and maintaining the data ingestion pipelines that power their AI Data Marketplace platform. You will work closely with cross-functional teams to design, build and optimise data assets, ensuring stability, traceability and high availability. Your expertise in Azure technologies, big data processing and ETL will directly contribute to delivering world-class data products and services to internal consumers. 




Responsibilities 






  • Identify opportunities to improve data assets and services within the AI Data Marketplace 








  • Design and develop data ingestion pipelines with focus on stability and optimisation 








  • Build, test and maintain robust data pipelines and processes 








  • Write resilient, fault-tolerant and modular code for high availability systems 








  • Research and implement new methods to enhance data products 








  • Collaborate across teams to ensure smooth transitions through ingestion and consumption phases 








  • Manage Azure Data Factory pipelines, activities, triggers and data flows 








  • Process large datasets using Azure Databricks, Apache Spark and PySpark 








  • Transform and integrate data from various sources using ETL processes 






Requirements 






  • 8-10 years of experience as a Data Engineer 








  • Expert knowledge of Azure Data Factory, including pipeline development and management 








  • Proficiency in Azure Databricks for data processing with notebooks, jobs and clusters 








  • Strong programming skills in Python, PySpark and SQL 








  • Experience with Apache Spark for big data processing 








  • Solid understanding of ETL processes and data transformation 








  • Familiarity with Azure storage solutions including Azure SQL Database, Blob Storage and Data Lake Storage 








  • Knowledge of data warehousing principles and scalable data modelling techniques 








  • Experience integrating multiple data sources in cloud environments 








  • Ability to write modular, testable code for production systems 






Benefits 




While specific benefits have not been disclosed, you will join a collaborative environment where technical excellence is valued and encouraged. You'll work alongside skilled professionals on meaningful projects that advance data engineering capabilities, with opportunities to research and implement innovative solutions that shape the future of their data marketplace platform. 




How to Apply 




To apply for this role please submit your CV using the form below or email [email protected] 




 



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