DataOps Specialist

Role overview

The DataOps Specialist is responsible for ensuring that data pipelines, workflows, and platform jobs run in a dependable, automated, and production-ready manner. This role supports data delivery where reliability, monitoring, recoverability, and operational discipline are essential for analytics platforms, data quality processes, and AI-related workloads.

Core responsibilities

• Keep data pipelines, scheduled jobs, workflows, and platform processes running reliably in production.
• Track workload execution, delays, failures, alerts, exceptions, and operational patterns.
• Assist with deployments, release activities, environment moves, configuration updates, and operational readiness.
• Build or support automation that reduces repetitive operational work for data processes.
• Maintain logging, monitoring, observability, alerting, and support workflows for data operations.
• Analyze pipeline failures, job issues, data loading problems, performance bottlenecks, and other incidents.
• Work with data engineering, platform, infrastructure, security, and support teams to drive issue resolution.
• Support validation, reconciliation, restart, recovery, rollback, and incident handling activities.
• Prepare and maintain runbooks, job calendars, support documentation, escalation steps, and known-issue records.
• Assist with production readiness reviews, service transition, hypercare, and handover to business-as-usual support.
• Spot recurring issues and suggest improvements in automation, monitoring, operational process, or platform design.
• Help ensure data quality checks are scheduled, observed, reported, and supportable.
• Encourage strong engineering practices around version control, testing, release management, and operational documentation.
• Contribute to the reliability, maintainability, and supportability of data platforms and data products.

Required skills and competencies

• Practical experience running data pipelines, ETL/ELT processes, data workflows, or analytics platform workloads.
• Hands-on exposure to orchestration and scheduling tools such as Airflow, Control-M, Azure Data Factory, Informatica, dbt Cloud, Dagster, Prefect, or similar platforms.
• Working knowledge of CI/CD, source control, release processes, and deployment methods for data solutions.
• Experience with monitoring, logging, alerting, job-failure analysis, and operational dashboards.
• Understanding of incident management, change management, problem management, service transition, and production support practices.
• Strong SQL and troubleshooting skills for data-related issues.
• Knowledge of data validation, reconciliation, quality checks, and pipeline control checks.
• Experience with cloud or on-premises data platforms such as Azure, AWS, Google Cloud, Snowflake, Databricks, SQL Server, Oracle, or equivalent systems.
• Ability to diagnose pipeline failures, load errors, performance issues, access problems, and environment-related incidents.
• Desirable scripting or automation experience with Python, Bash, PowerShell, or similar tools.
• Understanding of managing environments across development, test, staging, and production.
• Ability to create runbooks, support guides, schedules, escalation procedures, and known-issue documentation.
• Awareness of reliability, recoverability, observability, restartability, rollback, and supportability principles.
• Comfort working with engineering, platform, security, infrastructure, support, and business stakeholders.

Qualifications

A bachelor’s degree in Computer Science, Information Systems, Software Engineering, Computer Engineering, Cloud Engineering, Engineering, or another related technical field is required.

Fluency in both Arabic and English is mandatory.

We are looking for candidates with 3 to 6+ years of experience in DataOps, data engineering, DevOps, ETL/ELT operations, data platform operations, cloud data platforms, pipeline support, release management, monitoring, or production data support.

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

Preferred certifications

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