Senior DataOps Engineer

I'm partnered with an established FS company who are scaling their cloud-native data platform and building a modern, centralised data function to better support a wide community of Data Scientists, Analysts, and federated Data Engineers across the organisation.

They are looking for a Senior DataOps Engineer to help shape how data pipelines are run, monitored, governed, and optimised at scale.

This is an opportunity to join a growing team working at the heart of the organisation's data transformation, improving platform efficiency, enabling self-service, and ensuring data pipelines operate with the same discipline as production software.

The Role

As a Senior DataOps Engineer, you'll take a strategic, high-level view of the data platform while still diving deep when needed. You will focus on observability, automation, pipeline performance, operational excellence, and cloud cost optimisation.

You'll work cross-functionally with Data Engineering, DevOps, and FinOps teams, helping ensure that data services are reliable, scalable, secure and cost-effective, and that federated teams across the organisation can self-serve with confidence.

What You'll Be Doing

• Taking an overview of how pipelines run across the platform, improving performance and throughput
• Enhancing observability and monitoring across Azure-based data workloads
• Identifying bottlenecks and opportunities to streamline operational processes
• Using scheduling/orchestration tools to optimise workflows and improve run times
• Treating data pipelines like production-grade software with robust monitoring, automation, and scalability in mind
• Supporting incident management and helping federated teams resolve issues efficiently
• Driving efficiency through automation and reduction of manual operational overhead
• Working with FinOps practices to optimise spend and evaluate cost-performance trade-offs
• Advocating for better platform usage, adoption, and operational best practices

• Strong experience working with Azure cloud platform
• Background in data engineering and building/maintaining data pipelines
• Experience with pipeline monitoring, observability, and incident troubleshooting
• Strong automation mindset and ability to build resilient, self-healing data workflows

Nice to Have

• Knowledge of FinOps principles and cloud cost optimisation
• Experience with orchestration tools such as Azure Data Factory, Databricks Workflows, or Airflow
• Exposure to containerisation tools (Kubernetes, Docker)
• Experience with data cataloguing tools
• Familiarity with unit testing in data pipelines
• Awareness of MLOps practices