Data Engineer

Key Responsibilities:

Collaborate with clients and internal stakeholders to design and implement scalable data engineering solutions using the Microsoft Analytics ecosystem.
Develop and maintain data pipelines, ensuring efficient extraction, transformation, and loading (ETL) processes for structured and unstructured data.
Apply best practices in dimensional modeling to create robust and scalable data models for analytics and reporting.
Leverage tools such as Python, dbt, and SQL to develop advanced data transformation and integration processes.
Optimize data storage and retrieval mechanisms to support high-performance analytics workloads.
Implement and manage hybrid analytics workloads using Azure Cloud infrastructure, including networking and security configurations.
Monitor and troubleshoot data pipelines and workflows to ensure accuracy, reliability, and performance.
Collaborate with analytics and BI teams to ensure seamless integration of data assets into Power BI and other reporting platforms.
Stay informed about emerging trends and technologies in data engineering and cloud analytics to enhance solution delivery.

Required Qualifications:

Proven experience in data engineering with expertise in the Microsoft Analytics stack (Fabric, Synapse, SQL, SSAS, SSRS).
Strong proficiency in SQL, Python, and data modeling techniques.
Working knowledge of data extraction patterns and best practices.
Experience with ETL tools and frameworks, such as dbt.
Solid understanding of dimensional modeling and its application in analytics.
Strong problem-solving skills with a focus on delivering scalable and efficient solutions.
Excellent communication and collaboration skills, with experience in client-facing roles.

Preferred Qualifications:

Experience with Azure Cloud infrastructure, including hybrid analytics workload configurations and networking.
Certifications in Azure Engineering (e.g., Azure Data Engineer Associate, Azure Solutions Architect Expert).
Experience with Azure DevOps or GitHub for code repositories and deployment.
Familiarity with advanced analytics tools and concepts, including machine learning workflows.
Knowledge of additional data visualization tools such as Tableau or Qlik.
Experience with Agile methodologies for project management.

#DICE

Data Engineer

Quick Overview