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# Lead Data Engineer- AWS

- **Company:** Ecloud Labs
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Big Data, Computer Programming, Continuous Integration, Data Systems, Python (Programming Language), Data Streaming, Large Language Models, Apache Spark, Containerization, Data Lakes, Kubernetes, Infrastructure Automation Frameworks, Apache Flink, Apache Kafka
- **Published:** July 28, 2026
- **Apply:** https://www.dice.com/job-detail/018d15b2-7ab9-442a-975a-67a854e5b1b1

## About the Role

10+ years of experience in big data engineering

 - Strong experience with Kafka

 - Strong experience with Kubernetes

 - Experience with AWS

 - Experience building data lakes or lakehouse platforms

 - Experience with data mesh concepts

 - Strong programming skills in Python, Scala, or Java

 - Experience with Spark, Flink, or Beam

 - Experience with Airflow or orchestration tools

 - Understanding of data modeling and governance

 - Experience with CICD and infrastructure automation

 - Experience supporting AI and machine learning workloads including large language models such as Claude or similar platforms

 - Strong communication and leadership skills

## Description

As part of the Big Data Engineering and Analytics organization, this role will focus on enabling a modern data lake and data mesh architecture that supports high volume, high velocity, and high variety data across enterprise and platform domains.

The Lead Data Engineer will bring deep hands-on expertise in Kubernetes, Kafka streaming platforms. This role partners closely with data architects, analytics teams, and platform engineering to deliver reliable, governed, and high-quality data solutions.

Responsibilities

 - Lead the design and implementation of scalable big data pipelines for batch and real time processing

 - Build and operate streaming data platforms using Kafka

 - Design and deploy cloud native data solutions across AWS

 - Develop and manage containerized workloads using Kubernetes

 - Enable data mesh architecture with domain oriented data products

 - Design and implement data lake and lakehouse architectures

 - Ensure data quality, reliability, and observability

 - Implement governance capabilities including metadata and lineage

 - Collaborate with analytics, AI, and business teams

 - Optimize performance and cost efficiency


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