Nagarro
Information Technology And Services
StaffEngineer-DataOpsEngineer
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“Staff Engineer - DataOps Engineer at Nagarro. Skills: DataOps, DevOps, SRE, SQL, Python, Cloud Platforms, CI/CD, IaC, Monitoring Tools. Manage and support data pipelines, ETL processes, and analytics platforms. Ensure reliability, accuracy, and accessibility of data pipelines and platforms”
What You'll Achieve.
Ensuring the reliability, automation, and performance of our analytics and data platforms; Sustain and optimize data-driven environments; Ensure SLA adherence
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
8+ years in DataOps, Data Engineering Operations, or Analytics Platform Support, Proficiency in SQL, Proficiency in Python/Shell scripting for automation and data diagnostics, Experience with cloud platforms (AWS exposure to Azure/GCP a plus), Familiarity with CI/CD tools (Jenkins, Azure DevOps), Familiarity with version control (Git), Familiarity with IaC frameworks (Terraform, Ansible), Working knowledge of monitoring tools (Datadog, Grafana, Prometheus), Understanding of containerization (Docker, Kubernetes) concepts, Grasp of data governance, observability, and quality frameworks, Experience in incident management, Experience in operational metrics tracking (MTTR, uptime, latency), Python (Strong), SQL (Strong), DevOps - AWS (Strong), DevOps - Azure (Strong), DataDog
Nice to Have
Azure/GCP cloud platform experience
What You'll Do.
Manage and support data pipelines
and analytics platforms
and accessibility of data pipelines and platforms
Execute data validation
and performance tuning
Implement monitoring and observability
Integrate data deployments within CI/CD pipelines
Apply infrastructure-as-code principles for provisioning and automation
Support incident and request management
Maintain data governance and protection standards
Participate in Agile ceremonies
How You'll Work.
Team & Collaboration
Collaborate with DevOps and Infra teams; Align with cross-functional delivery squads
Process & Methodology
Agile ceremonies within Scrum/Kanban models
Full Job Description
We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 36 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in! Hi Team, We are seeking a DataOps Engineer to join Tech Delivery and Infrastructure Operations teams, playing a key role in ensuring the reliability, automation, and performance of our analytics and data platforms. This role is primarily DataOps-focused, combining elements of DevOps and SRE to sustain and optimize data-driven environments across global business units. You will manage end-to-end data operations from SQL diagnostics and data pipeline reliability to automation, monitoring, and deployment of analytics workloads on cloud platforms. Key Responsibilities * Manage and support data pipelines, ETL processes, and analytics platforms, ensuring reliability, accuracy, and accessibility * Execute data validation, quality checks, and performance tuning using SQL and Python/Shell scripting - Implement monitoring and observability using Datadog, Grafana, and Prometheus to track system health and performance * Collaborate with DevOps and Infra teams to integrate data deployments within CI/CD pipelines (Jenkins, Azure DevOps, Git) * Apply infrastructure-as-code principles (Terraform, Ansible) for provisioning and automation of data environments * Support incident and request management via ServiceNow, ensuring SLA adherence and root cause analysis * Work closely with security and compliance teams to maintain data governance and protection standards * Participate in Agile ceremonies within Scrum/Kanban models to align with cross-functional delivery squads Required Skills & Experience * 8+ years in DataOps, Data Engineering Operations, or Analytics Pl
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