Senior Data Platform Engineer

Senior Data Platform Engineer — Cloudera, AWS & Kubernetes

The mission We are looking for a battle-tested Data Platform Engineer who can build, operate, troubleshoot, and evolve large-scale data platforms across on-premises Cloudera environments and cloud-native AWS/Kubernetes architectures. This is not a dashboard or SQL-only role. You will work where distributed compute, storage, networking, Kubernetes, and production data pipelines meet. You must be comfortable tracing a failed workload from the application layer through Spark or Flink, Kubernetes operators, HDFS/Hive, infrastructure, and AWS services. The goal is to help us evolve safely from on-premises, VM, and EC2-based platforms into resilient, observable, cloud-native data systems.

What you'll do

• Build and operate production data platforms across Cloudera on premises, Cloudera cloud environments, AWS EMR, and Kubernetes.
• Design batch and streaming pipelines using Spark, Flink, Hive, and related technologies.
• Transform large datasets through filtering, sorting, joining, aggregation, partitioning, enrichment, and restructuring.
• Work with Parquet, Avro, JSON, CSV, and other delimited or semi-structured formats.
• Design storage, partitioning, compression, retention, and lifecycle strategies across HDFS, Hive, and object storage.
• Design and maintain Hive schemas, tables, partitions, metadata, and data models.
• Administer Cloudera clusters, including installation, upgrades, configuration, scaling, patching, security, backup, and recovery.
• Troubleshoot unhealthy services, failed jobs, resource contention, data skew, small-file problems, metadata issues, and storage bottlenecks.
• Tune Spark and Flink workloads for memory, CPU, parallelism, shuffle behavior, checkpointing, and recovery.
• Operate AWS services such as EMR, S3, IAM, EC2, EKS, CloudWatch, KMS, and supporting networking services.
• Deploy and operate data workloads on Kubernetes using operators, Helm, custom resources, and GitOps-based delivery.
• Help migrate workloads from physical or virtual machines and EC2 into AWS and Kubernetes-based platforms.
• Separate compute from storage where appropriate while accounting for performance, resilience, security, and cost.
• Build monitoring, alerting, capacity management, and operational runbooks for critical data services.
• Automate platform provisioning and configuration using Terraform, Ansible, scripting, and CI/CD.
• Support production incidents involving failed pipelines, delayed data, cluster degradation, storage pressure, or infrastructure failure.
• Work with data engineering, infrastructure, security, and application teams to resolve problems across ownership boundaries.

What we're looking for

• Strong hands-on experience administering Cloudera platforms in production.
• Experience with both on-premises Cloudera and cloud-based Cloudera deployments.
• Deep working knowledge of Hadoop, HDFS, Hive, YARN, Spark, and the wider distributed-data ecosystem.
• Experience building or operating production workloads using Apache Flink.
• Strong understanding of distributed data processing, including partitioning, shuffling, serialization, checkpointing, and failure recovery.
• Experience transforming large datasets using joins, aggregations, filtering, sorting, and schema evolution.
• Practical knowledge of Parquet, Avro, JSON, CSV, compression formats, and serialization tradeoffs.
• Experience designing data layouts for query performance, ingestion throughput, retention, and cost.
• Strong AWS experience, particularly with EMR, S3, EC2, EKS, IAM, CloudWatch, and KMS.
• Strong Kubernetes experience, including operators, controllers, Helm, scheduling, storage, networking, and workload troubleshooting.
• Experience migrating data platforms from on-premises or VM-based environments into AWS and Kubernetes.
• Ability to troubleshoot Linux, JVM, networking, storage, DNS, certificates, and resource-management issues.
• Experience with observability platforms and the ability to correlate infrastructure symptoms with data-pipeline failures.
• Ability to automate operational work using Python, Bash, Terraform, Ansible, or equivalent tools.
• Strong judgment around production changes, data integrity, access control, rollback, and recovery.

Production scenarios you should be able to handle

• A Spark job that ran in 40 minutes yesterday now takes four hours.
• A join creates severe data skew and repeatedly exhausts executor memory.
• HDFS is approaching capacity while NameNode health is degrading.
• Hive queries return incomplete results because partitions or metadata are inconsistent.
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