Data Platform Engineer

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Job Summary

Data Platform Engineers

If you love architecting distributed systems, solving complex infrastructure problems, and ensuring platforms “just work,” this role is for you.

Role Description

planet-scale Cloud Services infrastructure

embedded SRE model

This includes observability, SLO-driven operations, incident management, and end-to-end reliability engineering for services used globally.

You will work with a mix of open-source, vendor, and internally developed tools across globally distributed data centers.

Key Responsibilities

Platform Reliability & Operations

• Operate and support production data platforms at scale across internal and public cloud environments.
• Perform capacity planning, performance testing, disaster recovery planning, and manage distributed systems.
• Participate in a 24x7 on-call rotation to ensure service availability.

Embedded SRE Collaboration

• Work closely with partner engineering teams in a unified SRE model.
• Implement and enforce consistent incident management processes.
• Define and maintain user-journey–based SLOs with strong observability metrics.

Data Platform Engineering

• Architect, deploy, tune, and troubleshoot Big Data ecosystem tools:

• Apache Spark, Flink, Airflow, Hive, Hadoop/HDFS, Trino, Druid

  , etc.
• Manage storage and coordination systems such as

  Cassandra, Zookeeper, Redis

  , and cloud/block/blob storage technologies.

Automation & Tooling

• Reduce operational toil through automation and tooling improvements.
• Author and release code using

  Go, Python, Java, or Scala

  .
• Leverage configuration management, IaC, and CI/CD pipelines.

Infrastructure & Systems Engineering

• Work with Kubernetes-based environments at scale (EKS/GKE preferred).
• Manage Linux systems, containers, virtualization, and networking components.
• Diagnose complex issues using strong scientific and analytical troubleshooting.

Must-Have Skills

• Strong hands-on Kubernetes experience.
• Experience with Amazon EKS and/or Google Kubernetes Engine (GKE).
• Proficiency in Python (basic to mid-level coding capabilities).
• Experience supporting Linux systems in production environments.
• Expertise in Big Data technologies: Spark, Flink, Airflow, Hive, Hadoop/HDFS, Trino, Druid, etc.
• High-availability systems architecture knowledge.
• Hands-on experience with Terraform or other Infrastructure-as-Code tools.

Nice-to-Have Skills

• Java programming experience.
• Experience with Pulumi (Infrastructure-as-Code).