Data Engineer

## Overview With data being the fuel that drives our future - our strategies, policies, and business successes around data will define our future growth prospects. Unlocking the value available through the innovative use of data on behalf of consumers, businesses, and communities is key to our future. With our ongoing commitment to Visa’s Data Values and the responsible use of data, we at Visa have a bold vision to continue to grow and accelerate our data-related businesses and capabilities. Led by Visa’s first Chief Data Officer, the Global Data Office coordinates high-impact, complex, company-wide projects related to data business strategy, critical investments, policy development, and marketplace execution. ## About the Team We are a Data Engineering team within GDO, responsible for designing, building, and optimizing data pipelines and platforms that power Visa’s analytics, and decisioning capabilities. Our focus is on data quality, scalability, and reliability, enabling seamless integration of complex datasets across regions. ## About the role As a Data Engineer, you will play a critical role in designing and delivering scalable, high-quality data solutions that enable intelligent decision-making across Global Data Engineering capabilities. ## Key Responsibilities - Build and manage large scale ETL processes to generate card level aggregated attributes. - Ability to build modular and reusable codes considering the configurability and scalability while adhering to low-level design - Perform thorough unit testing of each development task and document the test results using standard defined templates - Build, schedule, and manage DAGs in Apache Airflow efficiently - Monitor data processing tasks in the Data pipelines in Airflow - Ensure quality control of data assets, reconciling data loaded across different stages in the data pipeline - Utilize strong data analytics skills to identify, discuss, and promptly fix data issues - Apply debugging skills to quickly rectify execution errors, ensuring minimal delays and impact on business operations - Collaborate and communicate with stakeholders for requirement understanding and clarifications - Maintain the highest level of quality and detail-oriented approach in daily tasks - Adhere to agile principle and actively participate in all the scrum ceremonies. ## Qualifications Basic Qualifications: - Minimum of 6 months of work experience or a bachelor's degree.Preferred Qualifications: - 2 or more years of work experience. - 1-3 yrs. work experience with a bachelor’s degree with specialization in computer science, data engineering, information science, electronics, etc. - Hands-on data engineering experience building and operating production data pipelines. - Extensive experience in Hadoop ecosystem and associated technologies, (For e.g. Apache Spark, Python, Pandas etc.), Open table formats (e.g. Iceberg) - Proficiency in Python, SQL, and PySpark. - Experience with Unix/Linux systems with scripting experience in Bash. - Experience with data pipeline and workflow management tools like Airflow, etc. - Experience with relational SQL and NoSQL databases - Practical experience with AWS data engineering services, especially S3, EMR, and Glue Catalog. - Strong analytic skills related to working with structured & unstructured datasets. - Proficiency in managing and communicating data warehouse plans to internal clients. - Experience with building processes supporting data transformation, data structures, metadata, dependency, and workload management. - Experience designing for data quality, resiliency, and observability, including validation checks, monitoring, alerting, and runbooks - Practical experience using generative AI tools such as Claude code or similar systems to accelerate coding, documentation, or incident analysis while applying appropriate review controls. - Strong communication skills and the ability to convert ambiguous business requirements into clear technical implementation plans. - Experience with platforms such as databricks or snowflake