We are looking for a DevOps Data Engineer who will play a critical role in designing, automating, and optimizing high-scale data infrastructure that supports real-time and batch data processing. This role is a hybrid between DevOps and Data Engineering, focusing on containerized data applications and cloud automation in a high-performance distributed environment.

You will work on cloud-native, high-scale data platforms by integrating DevOps principles with data engineering best practices. Technologies you will use include Kubernetes, Helm, Terraform, Kafka, Solr, Elasticsearch, and JVM-based applications (Spring Framework). If you enjoy working on high-throughput data systems, infrastructure automation, and scalable cloud solutions, this is the perfect opportunity!

Key Responsibilities

•   Data Infrastructure & Cloud Deployment- Design, deploy, and manage high-scale, cloud-native data platforms on GCP, AWS.
•   Automate provisioning, scaling, and maintenance of data services using Terraform, Helm, and Kubernetes.
•   Optimize and manage distributed data platforms (Solr).
•   Data Pipeline Reliability & Observability- Maintain and optimize real-time streaming and batch data pipelines using Kafka, DataFlow or PubSub .
•   Implement monitoring, logging, and alerting using  Datadog or Prometheus or OpenTelemetry.
  JVM-Based Application Deployment
•   Deploy and manage JVM-based applications (Spring Framework, Java microservices) in containerized environments.
•   Ensure reliable and scalable deployment of data processing services in Kubernetes clusters. CI/CD & Infrastructure as Code (IaC) 
• Automate deployments using CI/CD pipelines (Jenkins, GitHub Actions, ArgoCD).
• Implement GitOps workflows for managing data infrastructure and applications.
•   Security, Compliance & Data Governance
• Implement RBAC, encryption, and compliance policies for GDPR, CCPA, and SOC2.
• Automate data retention, backup, and disaster recovery strategies.
• Ensure auditability and traceability of data workflows.

Desired Skills and Experience

• 3+ years of experience in Infrastructure, DevOps, or Site Reliability Engineering (SRE) in high-scale production environments handling large-scale data systems.
• 3+ years of experience managing/owning a Kubernetes-based platform and deploying containerized applications at scale.
• Experience deploying and managing JVM-based applications (Spring Framework, Java microservices) in Docker and Kubernetes environments.
• Strong background in cloud-native data platforms, including experience with GCP  or AWS.
• Expertise in Infrastructure as Code (IaC) tools such as Terraform and  Helm for automating cloud infrastructure.
• Experience optimizing search and NoSQL databases such as HBase, Solr .
• Advanced knowledge of Linux OS and networking, including troubleshooting low-latency distributed systems.
• Deep experience in CI/CD automation using Jenkins, GitHub Actions or ArgoCD to manage data infrastructure and application deployments.
• Expertise in monitoring, observability, and performance tuning with tools like Datadog, OpenTelemetry or Prometheus and New Relic.
• Experience in public, private, and hybrid cloud architectures, preferably with AWS or GCP.

Preferred Qualifications (Nice to Have)

• Experience designing self-service tools for data infrastructure automation.
• Prior experience in Machine Learning infrastructure (MLOps) for AI-driven data pipelines.
• Knowledge of service mesh technologies such as Istio.
• Experience implementing cost-optimization strategies for large-scale data platforms.
• Experience with Python, Spark Streaming, Flink, Airflow, 

Why Join Us?
Work on cutting-edge cloud and data infrastructure at scale.
Collaborate with top engineers in DevOps & Data Engineering.
Competitive salary, benefits, and career growth opportunities.

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