Senior Data Engineer: Real-Time Pipelines on AWS

We are seeking a Mid–Senior Data Engineer with strong expertise in AWS-based data engineering, real-time streaming technologies, and enterprise-grade data quality frameworks. The ideal candidate will design, build, and optimize scalable batch and streaming data pipelines, implement robust data validation and monitoring processes, and support mission-critical analytics platforms. Key Responsibilities: Develop and maintain scalable ETL/ELT pipelines using AWS Glue, PySpark, and Python Design and manage real-time streaming solutions using Kafka, KSQL, and Apache Flink Implement and enforce comprehensive data quality frameworks, including validation, profiling, monitoring, and reconciliation Optimize data processing performance, scalability, reliability, and cost in cloud environments Collaborate with cross-functional teams to deliver reliable, production-grade data platforms and ensure data integrity across the pipeline Required Skills: Strong hands-on experience with Python and PySpark Proven expertise in AWS Glue, Lambda, and other cloud-native data services Solid experience with the Kafka ecosystem (topics, partitions, consumer groups, streaming patterns) Demonstrated experience building and supporting data quality frameworks (validation rules, reconciliation checks, profiling, anomaly detection) Strong understanding of distributed data processing and scalable architecture patterns Good-to-Have Skills: Experience with Apache Flink for real-time stream processing and stateful computations Knowledge of KSQL or other streaming SQL engines Exposure to CI/CD pipelines, IaC (Terraform/CloudFormation), and DevOps practices Familiarity with data lake/lakehouse architectures and table formats such as Iceberg, Delta, or Hudi Experience working in enterprise or financial data environments #J-18808-Ljbffr eOne Infotech