MatchMove - Data Engineer - Numpy/Pandas

You Will Get To

• Design, build, and maintain high-performance data pipelines that integrate large-scale transactional data from our payments platform, ensuring data quality, reliability, and compliance with regulatory requirements.
• Develop and manage distributed data processing pipelines for both high-volume data streams and batch processing workflows in a cloud-native AWS environment.
• Implement observability and monitoring tools to ensure the reliability and scalability of the data platform, enabling stakeholders to make confident, data-driven decisions.
• Collaborate with cross-functional teams to gather requirements and deliver business-critical data solutions, including automation of payment transactions lifecycle management, regulatory reporting, and compliance.
• Design and implement data models across various storage paradigms to support payment transactions at scale while ensuring efficient data ingestion, transformation, and storage.
• Maintain data integrity by implementing robust validation, testing, and error-handling mechanisms within data workflows.
• Ensure that the data platform adheres to the highest standards for security, privacy, and governance.
• Provide mentorship and guidance to junior engineers, driving innovation, best practices, and continuous improvement across the :
• 4-6 years of experience in backend development and/or data platform engineering.
• Proficiency in Python, with hands-on experience using data-focused libraries such as NumPy, Pandas,
  

• Strong expertise in AWS services (S3, Redshift, Lambda, Glue, Kinesis, etc.) for cloud-based data infrastructure and processing.
• Experience with multiple data storage models, including relational, columnar, and time-series databases.
• Proven ability to design and implement scalable, reliable, and high-performance data workflows, ensuring data integrity, performance, and availability.
• Experience with workflow orchestrators such as Apache Airflow or Argo Workflows for scheduling and automating data pipelines.
• Familiarity with Python-based data stack tools like DBT, Dask, Ray, Modin, and Pandas for distributed data processing.
• Hands-on experience with data ingestion, cataloging, and change-data-capture (CDC) tools.
• Understanding of DataOps and DevSecOps practices to ensure secure and efficient data pipeline development and deployment.
• Strong collaboration, communication, and problem-solving skills, with the ability to work effectively
  

• Experience in payments or fintech platforms is a strong plus, particularly in processing high volumes of transactional data.