WHAT YOU’LL BE WORKING ON

• Design and build complex data pipelines: Develop and manage data pipelines that collect, process, and store data from various sources.
• Data integration: Design processes to combine raw data from different sources to create consistent and machine-readable formats.
• Data quality and reliability: Implement methods to improve data reliability, efficiency, and quality across various data sources.
• Collaboration: Work with data scientists, analysts, and other stakeholders to support their data needs. Technical leadership and mentoring of Data Engineers
• Optimization: Review and optimize processes for execution time and resource utilization
• Data Analysis: Analyze business requirements and translate them into technical specifications for Data Engineers.
• Troubleshooting: Identify and resolve issues related to the data processes and models.

EDUCATIONAL BACKGROUND

• Bachelor’s degree in computer science, Information Technology, or a related field.

SOFT SKILLS

• Excellent analytical & problem-solving skills
• Possesses a high regard for quality work and customer support
• Desire and aptitude to develop business and industry domain knowledge
• Functions efficiently and with a sense of urgency in fast paced and multitasking environment
• Self-motivated as well as ability to work well in a team environment
• Excellent written and verbal communication skills

TECHNICAL SKILLS

• Experience: 8+ years in data engineering
• Experience with data modeling and data analysis.
• Proficiency in programming languages Python(pyspark), SQL
• Experience with database and data lake systems such as SQL Server, Oracle, Azure Fabric (Delta Tables) and Synapse
• Knowledge of ETL tools and processes. Deep knowledge of Spark based technologies
• Familiarity with data warehousing concepts and technologies.

PREFERRED QUALIFICATIONS:

• Experience with cloud-based data engineering solutions Azure and Microsoft Fabric
• Certifications in relevant Data Engineering technologies
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Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers. Their primary responsibility is to make data available, accessible, and secure to stakeholders.