Product Support Engineer - Hadoop

United States·CampbellRemoteFull-Timemid

OtherTechnical Support EngineerCustomer

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ABOUT US Acceldata is the market leader in Enterprise Data Observability. Founded in 2018 and backed by top investors including Insight Partners, March Capital, Lightspeed, Sorenson Ventures,

Technical Tools

OtherTechnical Support EngineerCustomer

ABOUT US

Acceldata is the market leader in Enterprise Data Observability. Founded in 2018 and backed by top investors including Insight Partners, March Capital, Lightspeed, Sorenson Ventures, Industry Ventures, and Emergent Ventures, we are a Series-C funded company headquartered in Silicon Valley.

Our Enterprise Data Observability Platform—the first of its kind—helps enterprises build and operate world-class data products by ensuring data is reliable, trusted, and ready to power today’s most critical technologies, including AI, LLMs, Analytics, and DataOps.

Delivered as a SaaS solution, Acceldata is trusted by leading global organizations such as HPE, HSBC, Visa, Freddie Mac, Manulife, Workday, Oracle, PubMatic, PhonePe (Walmart), Hershey’s, Dun & Bradstreet, and many more.

As a Product Support Engineer, you will own complex customer environments, mentor junior team members, and drive the reliability of Hadoop and Spark-based data processing systems. You will collaborate closely with Customer Engineering teams to deliver high-throughput Hadoop and Spark applications, solve complex data challenges during migration and upgrades, ensure reliability, and optimize post-migration system performance.

We’re seeking someone who can flex across both EST and PST time zones. This role also includes weekend coverage, so a willingness to adjust working hours as needed is important.

Troubleshooting: Provide tier-2/3 support for data or performance issues in Hadoop clusters across the entire technical stack.

Debugging: Conduct deep-dive debugging and optimization of Hadoop clusters, including NiFi, Impala, and Spark jobs.

Migration: Lead product support during ODP Hadoop migrations and upgrades, ensuring post-migration stability, addressing upgrades, and evolving technical hurdles.

Optimization: Design and optimize distributed Hadoop-based applications to ensure low-latency, high-throughput performance for big data workloads.

Experience: 5+ years of hands-on experience working with hadoop environments

Technical proficiency in core hadoop services (HDFS, YARN, and Hive/Impala) and good working knowledge of Kafka, NiFi, Ambari, and Cloudera Manager internals.

Extensive experience in troubleshooting and debugging hadoop components,

Linux: Advanced skills in configuring, tuning, and troubleshooting Red Hat and Debian-based distributions.

Strong desire to tackle complex technical problems in Hadoop and proven ability to do so with little or no direct daily supervision.

Good to have proficiency in Python, Bash, or Scala for system automation and performance monitoring.