Description
The project modernizes, maintains, and develops an eCommerce platform for a large US-based retail company serving millions of omnichannel customers each week. Product teams focus on Customer, Loyalty, Search and Browse, Data Integration, and Cart. Current priorities include onboarding new brands, re-architecture, database migrations, and migrating microservices to a unified cloud-native solution without disrupting business.
Tasks
•Implement a solution for a large retail company
•Support the processing of large data volumes and integrate the solution with the current architecture
Requirements
•8+ Years of overall experience
•3+ Years of recent, hands-on experience with Azure Data Factory and Synapse
•Strong expertise in designing and implementing conceptual, logical, and physical data models
•Strong knowledge of Microsoft Azure, including Azure Data Lake Storage, Azure Synapse Analytics, Azure Data Factory, Azure Databricks, and PySpark
•Extensive experience building scalable ETL/ELT pipelines for data lakes or data warehouses
•Ability to integrate data from databases, APIs, and external data providers using API integration or message queuing
•Proficiency in data warehousing solutions, including dimensional modeling, star schemas, Data Mesh, Data/Delta Lakehouse, and Data Vault
•Proficiency in SQL for complex queries, data transformations, and performance tuning on cloud-based data storage
•Experience integrating metadata and governance processes into cloud-based data platforms
•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
•Demonstrated ability to lead cross-functional engineering teams, define technical strategy and architecture, drive delivery of complex data platforms, mentor engineers, and communicate with stakeholders at all organizational levels
Nice to have
•Azure, Databricks, or other relevant technology certifications
•Bash/Unix/Windows shell scripting
•Elasticsearch
•Docker, Kubernetes
•Troubleshooting and performance tuning of data workflows and pipelines
•Collaboration with stakeholders and cross-functional teams
•Planning, estimating, and tracking feature implementation
•Computer Science and data science academic credentials
Conditions
No conditions specified in the posting.
