JOB SUMMARY
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 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).
Familiarity with Linux/Unix environments and shell scripting.
Understanding of data security, governance, and compliance standards.
Preferred Qualifications
Experience in Java , Scala, Python or Kafka.
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.
