Data Engineer Ab initio

We are seeking a highly skilled resource to design and implement high-performance, event-driven data pipelines, ensuring low-latency data processing and high availability system for the large credit card processing system. The ideal candidate will work with the Ab Initio ecosystem (GDE, EME, Conduct>It) to build stateful services that ingest, filter, and transform data from sources like Kafka or message queues, pushing updates to dashboards or downstream databases in near-real-time., * Create complex Ab Initio continuous flow graphs, including stateful joins, sliding time windows, and aggregations.

• Implement event-driven data pipelines using Kafka, MQ, and file streams.
• Ensure the resilience of continuous flows, including checkpointing and recovery, to guarantee “exactly-once” processing.
• Apply advanced Ab Initio components (e.g., Reformat, Rollup, Join, Partition) to ensure low-latency performance.
• Proactively monitor live production streams to ensure 24/7 reliability and troubleshooting data issues
• Develop ETL pipelines for batch and real-time data ingestion and transformation.
• Implement and ensure data validation, data security, integrity, and compliance across big data platforms.
• Monitor and troubleshoot performance issues in large-scale clusters.
• Collaborate with data scientists, analysts, and application teams to deliver high-quality data solutions.
• Automate workflows and improve operational efficiency using scripting and orchestration tools.

• Deep understanding of credit card process system
• Deep knowledge of GDE (Graphical Development Environment), EME (Enterprise Meta>Environment), Conduct>It, and Continuous Flows.
• Understanding of Kafka, message queues, and real-time stateful services.
• Proficiency in Unix/Linux shell scripting, SQL, and database technologies (e.g., Oracle, Teradata).
• Experience in Java , Scala, Python or Kafka is plus.
• Familiarity with Linux/Unix environments and shell scripting.
• Understanding of data security, governance, and compliance standards.
• Experience with cloud-based big data platforms
• Exposure to containerization (Docker, Kubernetes) for big data workloads.
• Knowledge of CI/CD pipelines for data engineering projects.