Azure Data Engineer

Writer Corporation

Responsibilities:

• You are detailed reviewing and analyzing structured, semi-structured and unstructured data sources for quality, completeness, and business value.
• You design, architect, implement and test rapid prototypes that demonstrate value of the data and present them to diverse audiences.
• You participate in early state design and feature definition activities.
• Responsible for implementing robust data pipeline using Microsoft, Databricks Stack
• Responsible for creating reusable and scalable data pipelines.
• You are a Team-Player, collaborating with team members across multiple engineering teams to support the integration of proven prototypes into core intelligence products.
• You have strong communication skills to effectively convey complex data insights to non-technical stakeholders.
• You have an experience working collaboratively in cross-functional teams and manage multiple projects simultaneously.

Skills:

• Advanced working knowledge and experience with relational and non-relational databases.
• Experience building and optimizing Big Data pipelines, architectures, and datasets.
• Strong analytic skills related to working with structured and unstructured datasets.
• Hands-on experience in Azure Databricks utilizing Spark to develop ETL pipelines.
• Strong proficiency in data analysis, manipulation, and statistical modeling using tools like Spark, Python, Scala, SQL, or similar languages.
• Strong experience in Azure Data Lake Storage Gen2, Azure Data Factory, Databricks, Event Hub, Azure Synapse.
• Familiarity with several of the following technologies: Event Hub, Docker, Azure Kubernetes Service, Azure DWH, API Azure, Azure Function, Power BI, Azure Cognitive Services.
• Azure DevOps experience to deploy the data pipelines through CI/CD.

Qualifications and Experience:

• Minimum 5-7 years of practical experience as Data Engineer.
• Bachelors degree in computer science, software engineering, information technology, or a related field.
• Azure cloud stack in-production experience.