Data Engineer

4 days ago

Greater Kolkata Area, India MatchMove Full time ₹ 9,00,000 - ₹ 12,00,000 per year

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,

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.

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