We are seeking a DataOps Engineer to support Tech Delivery and Infrastructure Operations teams by ensuring the reliability, automation, and performance of our analytics and data platforms.
The role focuses on DataOps, blending DevOps and SRE practices to sustain and optimize data environments across global business units.
You will oversee end-to-end data operations, from SQL diagnostics and pipeline reliability to automation, monitoring, and deployment of analytics workloads on cloud platforms, working closely with Data Engineering, Product, and Infrastructure teams to maintain scalable, secure, high-performing systems.Key ResponsibilitiesManage and support data pipelines, ETL processes, and analytics platforms, ensuring reliability, accuracy, and accessibilityExecute data validation, quality checks, and performance tuning using SQL and Python/Shell scriptingImplement monitoring and observability using Datadog, Grafana, and Prometheus to track system health and performanceCollaborate 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 environmentsSupport incident and request management via ServiceNow, ensuring SLA adherence and root cause analysisWork closely with security and compliance teams to maintain data governance and protection standardsParticipate in Agile ceremonies within Scrum/Kanban models to align with cross-functional delivery squadsQualifications8+ years in DataOps, Data Engineering Operations, or Analytics Platform Support, with good exposure to DevOps/SRE practicesProficiency in SQL and Python/Shell scripting for automation and data diagnosticsExperience with cloud platforms (AWS mandatory; exposure to Azure/GCP a plus)Familiarity with CI/CD tools (Jenkins, Azure DevOps), version control (Git), and IaC frameworks (Terraform, Ansible)Working knowledge of monitoring tools (Datadog, Grafana, Prometheus)Understanding of containerization (Docker, Kubernetes) conceptsStrong grasp of data governance, observability, and quality frameworksExperience in incident management and operational metrics tracking (MTTR, uptime, latency)Flexible to work during Eastern Time (ET) business hours.