Degree in Information Technology or Computer Engineering, Computer Science or a related field.
Between 3 to 9 years of experience in designing and implementing scalable software and data-driven services using Big Data technologies, data storages (e.G. relational databases, key-value stores), data warehouses, data lakes, data lakehouses and related data processing technologies, as well as programming languages such as Spark / Python / Java / Scala (for real-time and batch processing).
Experience in building and optimizing data pipelines that process large datasets using Spark and cloud computing environment.
Experience with working on cloud environments especially with data related services, for example BigQuery, BigTable, Dataproc, GCS, Composer, GKE on Google Cloud Platform and low-code/no-code (e.G YAML) approaches for ETL tools.
Experience in Business Intelligence and SQL utilization on analyzing large datasets.
Experience with workflow management platforms (like Airflow) and collaborative platforms (like Jupyter) Some experience in the domain of information retrieval, information extraction or machine learning/AI concepts and technologies is a plus.
Good knowledge of Agile methodologies (SAFe / Scrum).
In-depth understanding of software development processes, CI/CD methodologies, Devops overview and quality assurance practices.
(e.G. XLRelease/XLDeploy, Terraform, Jenkins, GIT, Bitbucket, etc) Microsoft Azure and PowerBI is a plus.
Familiarity with AI concepts and technologies is a plus but not a primary focus of the role.