Responsibilities:

• Design and develop robust, scalable, and high-performance data pipelines, ensuring data integrity, reliability, and efficiency.
• Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and implement solutions accordingly.
• Optimize data infrastructure and architecture to improve the overall performance and reliability of our data systems.
• Implement and maintain ETL (Extract, Transform, Load) processes to efficiently process and transform large volumes of data from diverse sources.
• Develop and maintain data models, ensuring their accuracy, efficiency, and accessibility for analytics and reporting purposes.
• Perform data profiling, validation, and quality assurance to identify and rectify data anomalies or inconsistencies.
• Troubleshoot and resolve issues related to data pipeline failures, performance bottlenecks, and data quality problems.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Minimum of 7 years of professional experience in data engineering roles, with a focus on building and optimizing data pipelines and infrastructure.
• Strong proficiency in Python and SQL, with the ability to write efficient and optimized code.
• In-depth knowledge of data modeling concepts and experience with implementing and maintaining data models.
• Experience with at least one ETL tool, such as IBM DataStage, Talend, or Informatica.
• Familiarity with data warehousing concepts and different modeling techniques, such as dimensional modeling and data vault.