Key Responsibilities Design, develop, and maintain new data capabilities and infrastructure for Mastercard's Sustainable Technology Internal Data Lake. Create new data pipelines, data transfers, and compliance-oriented infrastructure to facilitate seamless data utilization within on-premise/cloud environments. Identify existing data capability and infrastructure gaps or opportunities within and across initiatives and provide subject matter expertise in support of remediation. Collaborate with technical teams and business stakeholders to understand data requirements and translate them into technical solutions. Work with large datasets, ensuring data quality, accuracy, and performance. Implement data transformation, integration, and validation processes to support analytics/BI and reporting needs. Optimize and fine-tune data pipelines for improved speed, reliability, and efficiency. Implement best practices for data storage, retrieval, and archival to ensure data accessibility and security. Troubleshoot and resolve data-related issues, collaborating with the team to identify root causes. Document data processes, data lineage, and technical specifications for future reference. Participate in code reviews, ensuring adherence to coding standards and best practices. Collaborate with DevOps teams to automate deployment and monitoring of data pipelines. Additional tasks as required. Qualifications Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field. Proven experience as a Data Engineer / Scientist or similar role. Deep understanding & expertise in data engineering, ETL/ELT processes, data warehousing, and data modeling. Strong command of data integration techniques and data quality management. Hands-on experience with data technologies such as Databricks, Spark, Python, SQL, Hadoop, Airflow. Familiarity with cloud platforms and services, such as AWS or Azure. Excellent analytical, problem-solving skills and ability to provide innovative data solutions. Exceptional interpersonal skills with proven experience in relationship building and partnering, must work well in both team/individual settings and must be able to work with a geographically dispersed team. Strong written and oral communication skills. Attention to detail is a must. Motivated self-starter with ability to excel at multi-tasking in a fast-paced environment and able to function under pressure with a high degree of initiative to drive results. Ability to quickly learn and implement new technologies and perform POC to explore best solutions for problem statements. Flexibility to work as a member of a matrix-based diverse and geographically distributed project team. All About You 0.6 -1.5 years of experience in Data Engineering or Warehouse-related projects. Expertise in Data Engineering and Data Analysis: implementing multiple end-to-end Data Engineering or Warehouse projects in Big Data environment. Experience building data pipelines through Spark with Scala/Python/Java in Databricks or Hadoop environment Experience working with databases like MS SQL Server or Oracle, and strong SQL knowledge. Experience automating data flow processes in a Big Data environment using Airflow or similar tools. Experience working in Agile teams.