Azure Data Engineer

Job Role: Azure Data Engineer 

Experience: 4+ years

About Dataeaze- 

Dataeaze systems are focused on making it easy for organizations to work with data. We help organizations build "data driven" software products, by incorporating domain knowledge and leveraging machine learning. With artificial intelligence and deep learning techniques, we build "software 2.0" data pipelines for all kinds of data -- streaming or big data, structured or unstructured.

Technical expertise related to data engineering

- Excellent business communication

- Excellent team coordination

- Extensive Knowledge in languages like => Python, Shell, Bash, etc...

- Extensive hands on experience and Knowledge in SQL

- Excellent problem solving skills

- Excellent knowledge in data engineering terminologies and concepts

- Excellent knowledge and hands on experience in Spark + PySpark

- Good knowledge about cloud platform (preferably azure)

- hands-on experience designing, planning, productionizing, maintaining and documenting reliable and scalable

data infrastructure and data products in complex environments

- Hands on experience with:

*Data bricks and using Spark for data processing (batch and/or real-time)

*Languages : Python, Scala, SQL

- Cloud platforms : Azure (ideally) or AWS

- Azure Data Factory

- Azure Data Lake, Azure SQL DB, Synapse, and Cosmos DB

- Designing data solutions in Azure incl. data distributions and partitions, scalability, disaster recovery and high availability

- Azure Devops (or similar tools) for source control & building CI/CD pipelines

- Experience designing and implementing large-scale distributed systems

- Deep knowledge and hands-on experience in technologies across all data lifecycle stages

expertise related to data engineering

- Excellent business communication

- Excellent team coordination

- Extensive Knowledge in languages like => Python, Shell, Bash, etc...

- Extensive hands on experience and Knowledge in SQL

- Excellent problem solving skills

- Excellent knowledge in data engineering terminologies and concepts

- Excellent knowledge and hands on experience in Spark + PySpark

- Good knowledge about cloud platform (preferably azure)

- hands-on experience designing, planning, productionizing, maintaining and documenting reliable and scalable

data infrastructure and data products in complex environments

- Hands on experience with:

*Data bricks and using Spark for data processing (batch and/or real-time)

*Configuring Delta Lake on Azure Data bricks

*Languages : Python, Scala, SQL

- Cloud platforms : Azure (ideally) or AWS

- Azure Data Factory

- Azure Data Lake, Azure SQL DB, Synapse, and Cosmos DB

- Designing data solutions in Azure incl. data distributions and partitions, scalability, disaster recovery and high availability

- Azure Devops (or similar tools) for source control & building CI/CD pipelines

- Experience designing and implementing large-scale distributed systems

- Deep knowledge and hands-on experience in technologies across all data lifecycle stages