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.

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