Product Specialist - Hadoop - acceldata

Position Summary:

We are seeking a Product Specialist - Hadoop SME (Subject Matter Expert) who will be responsible for designing, optimising, migrating and scaling Hadoop and Spark-based data processing systems. This role involves hands-on experience with Hadoop and other core data operations, focusing on building resilient, high-performance distributed data systems. You will collaborate with customer engineering teams to deliver high-throughput Hadoop and Spark applications and solve complex data challenges in migration, upgrades, reliability, and optimise post-migration system performance.

If you’re passionate about working in fast-paced, dynamic environments and want to be part of the cutting edge of data solutions, this role is for you.

We’re looking for someone who can:

• Design and optimise distributed Hadoop-based applications , ensuring low-latency, high-throughput performance for big data workloads.
• Troubleshooting : Provide expert-level support for data or performance issues in Hadoop and Spark jobs and clusters.
• Data Processing Expertise : Work extensively with large-scale data pipelines using Hadoop and Spark's core components
• Performance Tuning : Conduct deep-dive performance analysis, debugging, and optimisation of Impala and Spark jobs to reduce processing time and resource consumption.
• Cluster Management : Collaborate with DevOps and infrastructure teams to manage Impala and Spark clusters on platforms like Hadoop/YARN, Kubernetes, or cloud platforms (AWS EMR, GCP Dataproc, etc.).
• Migration : Provide dedicated support to ensure the stability and reliability of the new ODP Hadoop environment during and after migration. Promptly address evolving technical challenges and optimise system performance after migration to ODP.
• This role requires flexibility to work in rotational shifts, based on team coverage needs and customer demand.

Candidates should be comfortable supporting operations in a 24/7 environment and be willing to adjust their working hours accordingly.