Data Engineer

As a Senior Data Engineer II at FedEx in Chennai, India, you will be instrumental in designing, developing, and maintaining robust data pipelines and systems. Your responsibilities will include ensuring data quality, accessibility, and integrity, alongside creating efficient data models to support business intelligence and analytics. You will manage and optimize ETL processes, administer both SQL and NoSQL databases, and leverage big data technologies like Hadoop and Spark to process large-scale datasets. This role also involves implementing real-time data processing solutions, ensuring adherence to data governance and security standards, including access controls and encryption. Collaboration with cross-functional teams, including data scientists, analysts, and software engineers, is key to understanding data requirements and integrating data systems. You will also be responsible for documenting data architecture and processes and delivering data-driven insights. A Bachelor's degree in Computer Science, MIS, or a related field, along with a specific business accreditation for Business Intelligence, is required. We are looking for candidates with approximately five years of relevant work experience in data engineering.

• Design and implement scalable data pipelines for efficient data collection, processing, and storage.
• Ensure high standards of data quality, integrity, and accessibility for all data assets.
• Develop and maintain data models that underpin business analytics and reporting capabilities.
• Collaborate closely with data scientists, analysts, and business stakeholders to define data requirements and design optimal schemas.
• Build, manage, and automate ETL processes for seamless data integration and transformation.
• Administer and optimize performance of SQL and NoSQL databases, including monitoring and troubleshooting.
• Work with software engineers to integrate data solutions with various applications.
• Utilize big data platforms such as Hadoop and Spark for processing and analyzing massive datasets.
• Implement and manage distributed systems for real-time data processing needs.
• Uphold data governance policies and implement robust data security measures, including access controls and encryption.
• Maintain comprehensive documentation for data architecture, pipelines, and operational processes.
• Generate reports and provide actionable insights derived from data analysis.