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 team.

Requirements :

- Proficiency in Python, with hands-on experience using data-focused libraries such as NumPy, Pandas,

SQLAlchemy, and Pandera to build high-quality data pipelines.

- 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

across multiple teams and geographies.

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