Readiness to work until 8.00 pm CET (no need to do overtimes)
Overall years of experience required 8+ (at least 1+ year in an Architect position)
Strong, recent hands-on expertise with Azure Data Factory and Synapse is a must (3+ years).
Strong expertise in designing and implementing data models, including conceptual, logical, and physical data models, to support efficient data storage and retrieval.
Strong knowledge of Microsoft Azure, including Azure Data Lake Storage, Azure Synapse Analytics, Azure Data Factory, and Azure Databricks, pySpark for building scalable and reliable data solutions.
Extensive experience with building robust and scalable ETL/ELT pipelines to extract, transform, and load data from various sources into data lakes or data warehouses.
Ability to integrate data from disparate sources, including databases, APIs, and external data providers, using appropriate techniques such as API integration or message queuing.
Proficiency in designing and implementing data warehousing solutions (dimensional modeling, star schemas, Data Mesh, Data/Delta Lakehouse, Data Vault)
Proficiency in SQL to perform complex queries, data transformations, and performance tuning on cloud-based data storages.
Experience integrating metadata and governance processes into cloud-based data platforms
Certification in Azure, Databricks, or other relevant technologies is an added advantage
Experience with cloud-based analytical databases.
Experience with Azure MI, Azure Database for Postgres, Azure Cosmos DB, Azure Analysis Services, and Informix.
Experience with Python and Python-based ETL tools.
Experience with shell scripting in Bash, Unix or windows shell is preferable.
Demonstrated ability to lead cross-functional engineering teams, define technical strategy and architecture, drive delivery of complex data platforms, mentor engineers, and effectively communicate with stakeholders at all organizational levels.
Experience with Elasticsearch
Familiarity with containerization and orchestration technologies (Docker, Kubernetes).
Troubleshooting and Performance Tuning: Ability to identify and resolve performance bottlenecks in data processing workflows and optimize data pipelines for efficient data ingestion and analysis.
Collaboration and Communication: Strong interpersonal skills to collaborate effectively with stakeholders, data engineers, data scientists, and other cross-functional teams.
Ability to plan, estimate and track progress of implementing features
Computer Science and data science academic and education credentials
