Data Observability Engineer

A successful multinational technology business is looking for a Data Observability Engineer to join its growing data team in Central London. This role is hybrid – you’ll be able to work from home 2 days per week.

This is a high-impact role focused on improving data quality, reducing incidents, and building scalable observability across a modern enterprise data platform. You’ll help ensure data across the organisation is accurate, reliable, and trusted for critical business decision-making. You’ll take ownership of data reliability end-to-end, designing and implementing frameworks that monitor data health, detect anomalies, and enforce standards across complex data pipelines and platforms.

You’ll have experience in Data Engineering, Data Platform, or Data Observability roles, with strong SQL and Python skills and experience working in modern cloud-based data environments. Hands-on experience with data observability tools such as Grafana, Monte Carlo, or Acceldata, and data governance/quality platforms like Informatica, Collibra or Microsoft Purview is highly desirable. Experience within the Azure ecosystem (data lakes, ETL/ELT pipelines) would be a strong advantage.

Working across Data Engineering and Data Governance teams, you’ll help shift the organisation from reactive issue resolution to a proactive, reliability-led approach. You’ll define and manage data SLAs and SLOs, implement automated validation and observability tooling, and lead root cause analysis when issues occur.

You’ll also have experience building automated data validation frameworks, defining SLAs/SLOs, and working with distributed data architectures. Familiarity with CI/CD and Infrastructure-as-Code is beneficial. Most importantly, you’re a proactive problem-solver who enjoys working collaboratively and improving systems at their root cause.

If you’re looking to take ownership of data reliability in a complex enterprise environment and make a real impact on how data is trusted and used, apply today for immediate consideration.