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# AWS/Kafka Data Engineer - Senior Software Engineer

- **Company:** Capgemini
- **Location:** United States
- **Experience:** Expert
- **Salary:** $89,400.0 - $166,398.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Amazon Web Services, Databases, Information Engineering, Data Integration, Github, Python (Programming Language), PostgreSQL, Microsoft SQL Server, MySQL, Oracle (Applications), Service Discovery, Data Streaming, Transaction Data, Datadog, Spring-boot, Change Data Capture, Cloudformation, Data Lakes, Kubernetes, Apache Flink, Apache Kafka, Terraform, Stream Processing, Splunk, New Relic (SaaS), Data Pipelines, Docker, Microservices
- **Published:** October 16, 2026
- **Apply:** https://www.dice.com/job-detail/0469dbd1-fe73-4415-abbf-abf6b4bdb3af

## About the Role

* 10+ years of software/data engineering experience
 * Strong proficiency in AWS, Kafka, Java, Spring Boot, Python.
 * Expertise in AWS Managed Kafka (data & integration)
 * Design and configure AWS Managed Kafka
 * Experience working on PostgreSQL, MySQL, SQL Server, and Oracle

#LI-RM1 - Hybrid

## Description

* Design, build, and deploy robust, real-time, and batch data pipelines integrating disparate ERP with downstream data lakes and platforms.
 * Leverage Apache Kafka, Kafka Connect, and stream processing engines (Kafka Streams or Apache Flink) to process and transform high-volume change data capture (CDC) and transactional data.
 * Build resilient microservices and integration layers using Java and Spring Boot to orchestrate complex data flows and enterprise APIs.
 * Package applications into Docker containers and manage scalable deployments, service discovery, and container orchestration via Kubernetes.
 * Optimize multi-database environments across relational systems including PostgreSQL, MySQL, SQL Server, and Oracle.
 * Manage Infrastructure as Code (IaC) using Terraform or AWS CloudFormation, and build automated CI/CD deployment pipelines using GitHub Actions.
 * Set up end-to-end monitoring, APM, log management, and alerting using tools like DataDog, New Relic, or Splunk to ensure data pipeline health and transactional accuracy