DataOps Engineer

The Wow

This role is dedicated to the operational maintenance, governance, and delivery lifecycle of data assets. Unlike infrastructure-focused roles, the DataOps Engineer is responsible for user access provisioning, data platform permission management, deployment releases of analytical pipelines, and service observability (monitoring/alerting) to guarantee data availability and compliance without managing foundational cloud or network architecture.

This role involves active monitoring, incident resolution, design incident prevention plan and engagement with data requests while continuously improving our data processes and systems, providing essential support that ensures optimal data quality.

A taster of what you will be involved with: 

• Access & Provisioning: Manage and audit Service Accounts, AWS IAM policies for data pipelines, and Role-Based Access Control (RBAC) schemas and Role & Permission management on Snowflake.
• Release Management: Execute and automate deployment releases for data pipeline codebases, orchestration logic and database schemas.
• Application Observability: Configure, monitor, and optimize pipeline alerting thresholds in Amazon CloudWatch and Coralogix to log task execution failures and data delivery delays.
• Application Orchestration: Manage workflows, task schedules, and environment permissions using Apache Airflow.
• Incident Prevention: Actively design and implement preventive operations strategies, analysing system trends, pipeline bottlenecks, and job failures to eliminate systemic risks before SLA breaches occur.
• Advanced Monitoring & Alerting Engineering: Formulate and maintain sophisticated observability patterns, building real-time dashboards and threshold-based notification alerts inside Coralogix to flag delays, capacity exhaustion, or anomalies.
• Data Integrity & First-Line Response: Perform root-cause analysis on runtime pipeline anomalies and complex data flow errors, collaborating across analytics engineering teams to design permanent, scalable operational remediation.
• Perform rigorous investigations into data quality, identifying discrepancies and implementing corrective actions in collaboration with other teams.
• Collaborate on data engineering initiatives, focusing on the enhancement of existing processes and the development of new solutions to improve overall data workflow.
• Engage in incident monitoring and resolution, escalating more complex issues to second line support as necessary.