DataOps Specialist

Hands-on experience operating data pipelines, data workflows, data jobs, ETL/ELT processes, or analytics platform workloads
Practical experience with data orchestration and scheduling tools such as Airflow, Control-M, Azure Data Factory, Informatica, dbt Cloud, Dagster, Prefect, or equivalent
Working knowledge of CI/CD, version control, release management, and deployment practices for data solutions
Hands-on experience with monitoring, logging, alerting, job failure analysis, and operational dashboards
Working knowledge of incident management, change management, problem management, service transition, and production support practices
Strong SQL and data troubleshooting capability
Working knowledge of data validation, reconciliation, data quality checks, and pipeline control checks
Practical experience with cloud or on-prem data platforms such as Azure, AWS, Google Cloud, Snowflake, Databricks, SQL Server, Oracle, or equivalent
Ability to troubleshoot pipeline failures, data load issues, performance problems, access issues, and environment-related issues
Working knowledge of scripting or automation using Python, Bash, PowerShell, or equivalent is desirable
Practical understanding of environment management across development, test, staging, and production
Ability to create operational runbooks, support guides, job schedules, escalation procedures, and known issue documentation
Understanding of reliability, recoverability, observability, restartability, rollback, and supportability principles
Ability to work effectively with data engineering, platform, security, infrastructure, support, and business teams

A DataOps Specialist focuses on making data delivery reliable, automated, monitored, repeatable, recoverable, and supportable in production. This role is essential where data platforms, analytics, data quality checks, and AI pipelines must operate with production-grade discipline.

Support reliable operation of data pipelines, data jobs, workflows, data quality checks, and platform processes
Monitor scheduled data workloads, failures, delays, alerts, exceptions, and operational trends
Support deployment, release, environment promotion, configuration management, and operational readiness
Implement or support automation for repeatable data operations tasks
Support logging, monitoring, observability, alerting, and support procedures for data workflows
Investigate data pipeline failures, job errors, data load issues, performance issues, and operational incidents
Coordinate issue resolution across data engineering, platform, infrastructure, security, and support teams
Support data validation, reconciliation, restart, recovery, rollback, and incident response activities
Maintain operational runbooks, job schedules, support guides, escalation procedures, and known issue records
Support production readiness reviews, service transition, hypercare, and business-as-usual handover
Identify recurring operational issues and recommend automation, monitoring, process, or platform improvements
Support data quality monitoring by ensuring checks are scheduled, monitored, reported, and supportable
Promote engineering discipline across version control, testing, release management, and operational documentation
Help improve the reliability, supportability, and maintainability of data platforms and data products

Bachelor’s degree in Computer Science, Information Systems, Software Engineering, Computer Engineering, Cloud Engineering, Engineering, or a related technical discipline.

Arabic and English are mandatory.

3–6+ years of experience in DataOps, data engineering, DevOps, ETL/ELT operations, data platform operations, cloud data platforms, data pipeline support, release management, monitoring, or production data support.

Experience with data orchestration, CI/CD, observability, incident management, automation, and data quality monitoring is preferred.

Preferred certifications include:

DevOps Foundation or equivalent DevOps certification
DataOps Fundamentals or equivalent DataOps training
Microsoft Azure Data Engineer, AWS Data Engineer, Google Professional Data Engineer, or equivalent cloud data certification depending on the platform
Databricks, Snowflake, dbt, Airflow, Kubernetes, Docker, or equivalent platform/tool certification where relevant
ITIL 4 Foundation where production support and service management are important
Security, cloud operations, or site reliability engineering certification where relevant