Data Engineer II

Title: Data Engineer II Location: Arlington, VA (Onsite) Position Type: W2 contract Industry: Banking/Financials/Payments Required Education: Bachelor’s degree in a quantitative discipline such as Engineering, Mathematics, Finance, Business, or a related field. Equivalent practical experience may also be considered. Qualifications & Skills 9+ years of hands‑on data engineering experience building large‑scale ETL pipelines using Apache Spark, Hadoop, Python, and SQL. Strong background in the payments or financial sector. Proficiency in writing and optimizing SQL queries to retrieve, manipulate, and analyze data efficiently. Hands‑on experience with Apache Spark (PySpark, Spark SQL, Spark Streaming) and the Hadoop ecosystem (HDFS/Ozone, Hive, YARN). Understanding data modeling concepts and database design to support scalable data solutions. Familiarity with Python. Ability to analyze and troubleshoot data issues and provide solutions with minimal supervision. Basic knowledge of testing and validating data to ensure accuracy and consistency in data pipelines. Excellent verbal and written communication skills, with the ability to articulate complex ideas clearly to both technical and non‑technical stakeholders. Responsibilities Design, implement, and maintain scalable enterprise ETL processes and robust data pipelines for a global client base. Develop and optimize code for data processing, ensuring timely availability and accessibility of data. Leverage big data processing frameworks such as Apache Spark and Hadoop to build and optimize data pipelines. Collaborate with senior engineers to address data challenges and maintain high data quality. Assist in data delivery, working alongside Data Engineers and Analysts to support accurate, high‑value data solutions across various clients and industries. Build strong working relationships with team members and clients, contributing to local and global projects. Apply industry best practices, including version control, code reviews, and data validation, to ensure quality in data processes. Use SQL and other database technologies to optimize data processing and reduce the time required to handle large data sets. Design, implement, and maintain data pipelines using ETL frameworks, orchestration tools, and distributed data processing engines. Participate in automation of routine data tasks and streamlining processes. Comply with all Mastercard internal policies and adhere to external regulations. #J-18808-Ljbffr Accord Technologies Inc