Ab Initio Developer – Real-Time Data Processing

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.Key Responsibilities:• 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.Required Skills & Qualifications:• 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.