Remote Hadoop Data Engineer: Scale Big Data

BairesDev; United States; Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Remote work

Hardware home setup

Flexible hours

Parental leave

Career growth

Job summary

BairesDev is seeking a Software Engineer specialized in Apache Hadoop to design and build large-scale data solutions on Hadoop clusters. You will play a key role in creating robust storage and processing workflows with high performance and scalability for enterprise environments.

In this role, you will deploy production-grade Hadoop clusters, optimize MapReduce jobs, and collaborate with data teams to ensure reliability and data integrity.

Qualifications

• 4+ years of experience in Software Engineering, Data Engineering, or Big Data infrastructure.
• Proven expertise in managing and developing on Hadoop clusters and HDFS storage.
• Proficiency in MapReduce and YARN processing for large-scale data workloads.
• Hands-on experience with the Hadoop ecosystem, including Hive.
• Advanced proficiency in English.

Responsibilities

• Design, deploy, and manage production-grade Hadoop clusters, focusing on HDFS storage and YARN resource management.
• Develop and optimize efficient data processing jobs using MapReduce and Hive to handle petabyte-scale datasets.
• Maintain and scale the broader Hadoop ecosystem of tools to ensure seamless data integration and availability.
• Collaborate with data teams to implement best practices for data partitioning, compression, and cluster performance tuning.
• Ensure system reliability and data integrity through proactive monitoring and troubleshooting of distributed components.

Skills

Hadoop

HDFS

MapReduce

YARN

Hive

Big Data

Job description

BairesDev is seeking a Software Engineer specialized in Apache Hadoop to design and build large-scale data solutions on Hadoop clusters. You will play a key role in creating robust storage and processing workflows with high performance and scalability for enterprise environments.

In this role, you will deploy production-grade Hadoop clusters, optimize MapReduce jobs, and collaborate with data teams to ensure reliability and data integrity.