JOB DESCRIPTION

The Data Engineer plays a crucial role in the CLM team by designing, implementing, and maintaining the data infrastructure that supports the team’s analytics and data science initiatives. This position is responsible for developing and optimizing data pipelines, ensuring data quality and accessibility, and collaborating with data scientists and analysts to enable efficient data-driven decision-making. The Data Engineer will work on integrating data from various sources, implementing data governance practices, and creating scalable solutions that support the CLM team’s objectives in enhancing customer experiences and driving business growth.

TECHNICAL SKILLS:

• Proficiency in Python or Scala for data processing and automation.
• Experience with ETL tools (e.g., Apache NiFi, Talend, Informatica).
• Knowledge of data visualization tools (e.g., Tableau, PowerBI) to support data quality checks and pipeline monitoring.
• Familiarity with version control systems (e.g., Git) and CI/CD practices.
• Experience with container technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).
• Understanding of data security best practices and implementation

• Data Pipeline Development : Design, implement, and maintain efficient ETL (Extract, Transform, Load) processes to integrate data from various sources. Optimize existing data pipelines for improved performance and scalability.
• Data Warehouse Management: Develop and maintain the data warehouse architecture, ensuring it meets the needs of the CLM team. Implement data modeling techniques to optimize data storage and retrieval.
• Data Quality Assurance: Implement data quality checks and monitoring systems to ensure the accuracy and reliability of data used in analytics and reporting. Develop and maintain data documentation and metadata.
• Big Data Technologies: Utilize big data technologies (e.g., Hadoop, Spark) to process and analyze large volumes of customer data efficiently. Implement solutions for real-time data processing when required.
• Data Governance: Collaborate with relevant stakeholders to implement data governance policies and procedures. Ensure compliance with data privacy regulations and internal data management standards.
• Infrastructure Optimization: Continuously assess and optimize the data infrastructure to improve performance, reduce costs, and enhance scalability. Implement automation solutions to streamline data processes.
• Knowledge of data governance principles and data privacy regulations.