Product Support Engineer (Hadoop)

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Product Support Engineer (Hadoop) in United States.

This role sits at the intersection of advanced data engineering and high-touch customer support, focusing on complex Hadoop and Spark-based enterprise environments. You will act as a technical escalation point for mission-critical data platforms, helping global customers ensure reliability, performance, and scalability of large-scale distributed systems. The position involves deep troubleshooting, system optimization, and hands-on engagement with engineering teams during migrations and upgrades. You will work closely with customer engineers to resolve high-impact data challenges while improving overall platform stability. The environment is fast-paced and highly technical, requiring strong ownership and the ability to operate independently across time zones. This is a high-visibility role where your work directly influences the performance of enterprise data infrastructure used by global organizations.

Accountabilities

In this role, you will take ownership of complex Hadoop environments and ensure their reliability, performance, and successful evolution through upgrades and migrations. You will serve as a senior technical support resource while contributing to system optimization and cross-team collaboration.

Provide Tier 2/3 technical support for Hadoop and Spark-based systems, resolving complex data and performance issues
Perform deep-dive debugging across Hadoop ecosystems including HDFS, YARN, Hive/Impala, Kafka, and NiFi
Lead support efforts for Hadoop migrations and upgrades, ensuring system stability and performance post-transition
Optimize distributed data processing systems for low latency and high throughput in production environments
Collaborate with customer engineering teams to troubleshoot, analyze, and resolve large-scale data challenges
Mentor junior engineers and contribute to improving support processes and technical best practices
Work across time zones, including weekend coverage when required, to support global customers

Requirements

This position requires strong hands-on experience with Hadoop ecosystems and a proven ability to troubleshoot and optimize large-scale distributed systems independently. Candidates should be highly analytical, self-driven, and comfortable working in complex technical environments.

5+ years of hands-on experience working with Hadoop environments in production
Strong expertise in core Hadoop components including HDFS, YARN, Hive, and Impala
Experience with tools such as Kafka, NiFi, Ambari, and Cloudera Manager
Strong background in debugging, performance tuning, and system optimization of big data workloads
Advanced Linux skills (Red Hat, Debian) including configuration, tuning, and troubleshooting
Experience supporting or leading Hadoop migrations and upgrades (preferred)
Proficiency in Python, Bash, or Scala for automation and system monitoring (nice to have)
Strong problem-solving skills with the ability to work independently on complex technical issues
Excellent communication skills and ability to collaborate with engineering and customer teams

Benefits

Competitive salary range: $80,000 – $100,000 per year
Opportunity to work on mission-critical enterprise data systems used by global organizations
Exposure to cutting-edge technologies in data observability, AI, and large-scale analytics
Collaborative, high-caliber engineering environment focused on innovation and continuous learning
Growth opportunities in advanced data engineering and platform reliability domains
Inclusive and equal-opportunity workplace culture
Tools, resources, and support to help accelerate professional development
Remote-friendly collaboration across global teams and time zones

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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Product Support Engineer (Hadoop)

Requirements

Benefits

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