Data Observability Engineer

Overview

In this role you will own data reliability within a large, enterprise data platform. You will design and implement data observability frameworks, monitor data health, and drive proactive issue prevention across cross-functional teams. You’ll define data SLAs/SLOs and lead root cause analysis to ensure data is accurate, trusted, and ready for decision-making. This opportunity combines hands-on engineering with governance to scale observability in a hybrid-remote setup in Central London.

Responsibilities

• Design and implement data observability frameworks and monitoring across complex pipelines
• Monitor data health, detect anomalies, and enforce data standards
• Define and manage data SLAs/SLOs and automate validation tooling
• Lead root cause analysis when incidents occur and drive corrective actions
• Collaborate with Data Engineering and Data Governance teams to shift to a proactive reliability-led approach
• Contribute to CI/CD and Infrastructure-as-Code practices to improve deployment and automation
• Build automated data validation frameworks and support distributed data architectures

Key requirements

• Strong SQL and Python skills
• Experience in Data Engineering, Data Platform, or Data Observability roles
• Experience with cloud-based data environments
• Hands-on experience with data observability tools (Grafana, Monte Carlo, Acceldata)
• Experience with data governance/quality platforms (Informatica, Collibra, Microsoft Purview)
• Azure ecosystem experience (data lakes, ETL/ELT) advantageous
• Familiarity with CI/CD and Infrastructure-as-Code is beneficial
• Proactive problem-solver with collaborative mindset

• Proactive problem-solving
• Collaborative mindset
• Ownership and accountability
• SQL
• Python
• Grafana

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