regional senior data engineer

Overview

Who you'll be working for

What requirements you'll need to be eligible

• Bachelor’s degree holder in Computer Science, Data Science, Statistics, Mathematics or equivalent             
•   Minimum 5 years of experience in data engineering or a similar role.                           
•   Strong proven expertise in Azure Synapse Analytics, Databricks, and other Azure cloud technologies.             
• Proficiency in Python, PySpark, and other relevant programming languages for ETL automation.             
•   Experience in designing and implementing data models, including star schemas and other dimensional models.                           
• Experience in Data Management tools such as Data Lake, BI, ETL, Dashboards, Balanced Scorecards, etc.    
•  Excellent communication and presentation skills, to communicate complex analytical and technical content clearly and effectively.             
•   Demonstrated ability to work with large, complex datasets and build scalable data solutions.             
•  Track record of forming strong partnerships with cross-functional teams.
• Experience with data visualization tools and an understanding of how data engineering supports data analytics.                           

What you'll be doing on the job

• Design, develop, and maintain robust data pipelines and ETL processes to support data integration and transformation across multiple data sources.                            
• Design analytical solutions for strategic business problems through engagement and research.                       
• Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and translate them into technical solutions in meeting milestones, timelines, and deliverables for projects.                         
•   Implement and optimize data models, including star schemas, to facilitate efficient data querying and analysis.                 
•   Leverage Azure Synapse Analytics, Databricks, and other Azure services to build scalable data infrastructure.                   
•  Ensure data quality and consistency across all layers of the data lakehouse (bronze, silver, and gold layers).                       
•   Automate ETL processes using SQL, Python, PySpark, or other relevant technologies to streamline data ingestion and transformation.                 
•   Monitor and maintain the performance, security, and reliability of data systems.                         
•  Provide technical leadership and guidance on best practices for data engineering within the organization.                   
•   Document data pipelines, processes, and architecture to ensure transparency and knowledge sharing.                 
•    Work either individually or in project teams as a subject matter expert to deliver business need

Consultant Contact