Principal Data Engineer - Cloud

Full Time
First Shift (Day)
Experience: Fresh
Old Cairo, Egypt

1 Vacancy

Job Summary

We're on the lookout for exceptional individuals to join our team as Principal Data Engineers - Cloud. As a part of our team, you'll be driving change and making a significant impact in the gaming industry.

Lead the design, development, and maintenance of end-to-end data analytical solutions using Microsoft on-premises and cloud technologies.
Engage in collaborative requirement elicitation activities with our valued clientele.
Mentor and guide junior team members in the field of data engineering.
Showcase your expertise by delivering innovative analytical solutions to our clients and contributing to knowledge transfer within the organization.

Requirements

5+ years of experience as a data engineer.
Familiarity with analytical architectures including Data Warehouses, Data Lakes and Data Lakehouses.
Knowledge of Microsoft relational engines available – both on-premises (MS SQL Server) and on the cloud (Azure SQL, Azure Synapse Analytics Dedicated Pools).
Knowledge of query and workload optimization.
Knowledge of administrating and managing analytical solutions both on premises and on the cloud.
Knowledge of NoSQL offerings on Azure (e.g. CosmosDB).
Knowledge of setting up Data Lakes on ADLS gen 2 and Azure Synapse Analytics.
Knowledge of using SSIS and Azure Data Factory to set up ETL jobs.
Knowledge of setting up OLAP solutions using SSAS.
Familiarity with Azure Data Catalog to manage data lake metadata.
Knowledge of governance over Azure based analytical solutions including using tools such as Purview, Active Directory, Key Vault, Azure DevOps, etc.
Knowledge of using Azure Stream Analytics to capture and analyze data in real time.
Knowledge of setting up data warehouse solutions both on-premises (using MS SQL Server) and on the cloud (using Azure Synapse Analytics).
Knowledge of Dimensional Modeling (Dimensions, Facts, Slowly Changing Dimensions, Outriggers, Role Playing Dimensions, Junk Dimensions, Degenerate Dimensions, Multi-valued Dimensions, Transactional Facts, Periodic Snapshot Facts and Accumulating Snapshot Facts).
Knowledge of implementing common ETL patterns to support data warehouse workloads.

Principal Data Engineer - Cloud

Full Time
First Shift (Day)
Experience: Fresh
Old Cairo, Egypt

1 Vacancy

Job Summary

Lead the design, development, and maintenance of end-to-end data analytical solutions using Microsoft on-premises and cloud technologies.
Engage in collaborative requirement elicitation activities with our valued clientele.
Mentor and guide junior team members in the field of data engineering.
Showcase your expertise by delivering innovative analytical solutions to our clients and contributing to knowledge transfer within the organization.

Requirements

5+ years of experience as a data engineer.
Familiarity with analytical architectures including Data Warehouses, Data Lakes and Data Lakehouses.
Knowledge of Microsoft relational engines available – both on-premises (MS SQL Server) and on the cloud (Azure SQL, Azure Synapse Analytics Dedicated Pools).
Knowledge of query and workload optimization.
Knowledge of administrating and managing analytical solutions both on premises and on the cloud.
Knowledge of NoSQL offerings on Azure (e.g. CosmosDB).
Knowledge of setting up Data Lakes on ADLS gen 2 and Azure Synapse Analytics.
Knowledge of using SSIS and Azure Data Factory to set up ETL jobs.
Knowledge of setting up OLAP solutions using SSAS.
Familiarity with Azure Data Catalog to manage data lake metadata.
Knowledge of governance over Azure based analytical solutions including using tools such as Purview, Active Directory, Key Vault, Azure DevOps, etc.
Knowledge of using Azure Stream Analytics to capture and analyze data in real time.
Knowledge of setting up data warehouse solutions both on-premises (using MS SQL Server) and on the cloud (using Azure Synapse Analytics).
Knowledge of Dimensional Modeling (Dimensions, Facts, Slowly Changing Dimensions, Outriggers, Role Playing Dimensions, Junk Dimensions, Degenerate Dimensions, Multi-valued Dimensions, Transactional Facts, Periodic Snapshot Facts and Accumulating Snapshot Facts).
Knowledge of implementing common ETL patterns to support data warehouse workloads.