Descripción de la oferta
Project descriptionThe primary goal of the project is the modernization, maintenance and development of an eCommerce platform for a big US-based retail company, serving millions of omnichannel customers each week.Solutions are delivered by several Product Teams focused on different domains – Customer, Loyalty, Search and Browse, Data Integration, Cart.Current overriding priorities are new brands onboarding, re-architecture, database migrations, migration of microservices to a unified cloud‑native solution without any disruption to business.ResponsibilitiesData Engineer will be responsible for designing a solution for a big retail company, focusing on the support of processing of big data volumes and integrating the solution into the current architecture.SkillsReadiness to work until 8.00pm CET (no need to do overtime)Overall 8+ years of experience, at least 1+ year in a Lead/Architect positionStrong, recent hands‑on expertise with Azure Data Factory and Synapse (3+ years)Strong expertise in designing and implementing data models, including conceptual, logical, and physical models, to support efficient data storage and retrievalStrong 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 solutionsExtensive experience building robust and scalable ETL/ELT pipelines to extract, transform, and load data from various sources into data lakes or data warehousesAbility to integrate data from disparate sources, including databases, APIs, and external data providers, using appropriate techniques such as API integration or message queuingProficiency 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 storagesExperience integrating metadata and governance processes into cloud-based data platformsCertification in Azure, Databricks, or other relevant technologies is an added advantageExperience with cloud-based analytical databasesExperience with Azure MI, Azure Database for Postgres, Azure Cosmos DB, Azure Analysis Services, and InformixExperience with Python and Python-based ETL toolsExperience with shell scripting in Bash, Unix or Windows shell is preferableDemonstrated 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 levelsNice to haveExperience with ElasticsearchFamiliarity with containerization and orchestration technologies (Docker, Kubernetes)Ability to identify and resolve performance bottlenecks in data processing workflows and optimize data pipelines for efficient data ingestion and analysisStrong interpersonal skills to collaborate effectively with stakeholders, data engineers, data scientists, and other cross-functional teamsAbility to plan, estimate and track progress of implementing featuresComputer Science and data science academic and education credentialsLanguagesEnglish: B2 Upper Intermediate
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