Product Support Engineer (Hadoop)
About The Position
Acceldata is a Silicon Valley-based company founded in 2018, recognized as the market leader in Enterprise Data Observability. They have developed the world's first Enterprise Data Observability Platform, which is crucial for building and operating data products, especially in areas like AI, LLMs, Analytics, and DataOps. Acceldata's SaaS solutions provide mission-critical capabilities for trusted and reliable data, serving global customers such as HPE, HSBC, Visa, and Workday. The company is Series-C funded with investors including Insight Partners and Lightspeed. As a Product Support Engineer, you will be responsible for managing complex customer environments, mentoring junior team members, and ensuring the reliability of Hadoop and Spark-based data processing systems. This role involves close collaboration with customer engineering teams to deliver high-throughput applications, resolve data challenges during migrations and upgrades, and optimize system performance post-migration.
Requirements
- 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
- Advanced skills in configuring, tuning, and troubleshooting Red Hat and Debian-based distributions (Linux).
Nice To Haves
- Proficiency in Python, Bash, or Scala for system automation and performance monitoring.
Responsibilities
- Strong desire to tackle hard technical problems in Hadoop and proven ability to do so with little or no direct daily supervision.
- Provide tier-2/3 support for data or performance issues in Hadoop clusters across the entire technical stack.
- Conduct deep-dive debugging and optimisation of Hadoop clusters, including NiFi, Impala, and Spark jobs.
- Lead product support during ODP Hadoop migrations, upgrades, ensuring post-migration stability, addressing upgrades and evolving technical hurdles.
- Design and optimise distributed Hadoop-based applications to ensure low-latency, high-throughput performance for big data workloads.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
Number of Employees
101-250 employees
