DataOps Specialist

Required Skills & Competencies

• 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, Power Shell, 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 Data Ops 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.