What are the responsibilities and job description for the Sr. Data Engineer – AWS & Streaming - Full Time position at Sabio infotech?
Job Title: Sr. Data Engineer – AWS & Streaming
Location: Fort Mill SC/Charlotte, NC/New York, NY (2-3 Days Hybrid)
Position: Full-Time
Duration: 6 Months to Long Term
Experience level – 10 Years
Role Summary:
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.
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
- Build event-driven workflows using AWS Lambda
- 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