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Ab Initio Data Engineer

Job in Elizabeth, Union County, New Jersey, 07215, USA
Listing for: Micasa Global
Full Time position
Listed on 2026-08-04
Job specializations:
  • IT/Tech
    Data Engineering, Unix/Linux, Cloud Computing: Infrastructure & Operations
Salary/Wage Range or Industry Benchmark: 120000 - 150000 USD Yearly USD 120000.00 150000.00 YEAR
Job Description & How to Apply Below
  • 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.
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