Senior Data Engineer: Streaming & Cloud on AWS

Dexian; North Carolina; On-site; USD 140,000 - 190,000; Full time

Job summary

Dexian is seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines powering analytics and data products. The role emphasizes cloud-native solutions on AWS, distributed systems, and hands-on stream processing with Apache Flink using Python and Java.

You will design and maintain batch and real-time pipelines, implement ingestion with AWS DMS, Kafka, and Lambda, and work with Aurora PostgreSQL for data persistence. Collaboration with analytics and BI teams is essential.

Qualifications

• 5+ years of data engineering or backend engineering experience.
• Strong hands-on Python and Java in distributed systems.
• Experience building streaming apps with Flink and Kafka.
• Solid experience with AWS data services.
• Strong SQL and relational data modeling skills.
• Experience with large-scale distributed data systems.

Responsibilities

• Design, build, and maintain scalable data pipelines for batch and real-time workloads.
• Develop and optimize data processing jobs using PySpark, Python, and Java.
• Build stream processing apps with Apache Flink.
• Implement reliable data ingestion with AWS DMS, Kafka, and AWS Lambda.
• Design data persistence with Aurora (PostgreSQL) and related AWS services.
• Monitor, debug, and optimize pipelines for performance and cost.
• Collaborate with product, analytics, and platform teams to translate requirements into tech solutions.

Skills

Python

Java

PySpark

Apache Flink

Kafka

AWS Glue

AWS DMS

AWS Lambda

Amazon Aurora

SQL

Distributed systems

Tools

AWS Glue

AWS DMS

Kafka

Apache Flink

Qlik

Job description

Dexian is seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines powering analytics and data products. The role emphasizes cloud-native solutions on AWS, distributed systems, and hands-on stream processing with Apache Flink using Python and Java.

You will design and maintain batch and real-time pipelines, implement ingestion with AWS DMS, Kafka, and Lambda, and work with Aurora PostgreSQL for data persistence. Collaboration with analytics and BI teams is essential.