Senior Analytics Engineer - Operations

About the role

Company & Department Context The Financial Times (FT) is one of the world's leading business news and information organisations. We are recognised globally for our authority, integrity and accuracy. We provide a broad range of essential services, including news, comment, data and analysis, to the growing audience of internationally minded business people. We think the Financial Times is a great place to work: we have a technical stack that is cutting edge - employing cloud-based architectures and using a wide range of open source and commercial technologies; our product development methodologies are very current - using a data driven approach, empowering everyone to understand and to delight our customers; our engineers choose the tools they need and are able to influence the development process. Importantly, we ensure staff also have the ability to achieve a good work life balance. As the digital business evolves and grows, there is ever more need to provide high quality and timely business intelligence and customer insight to inform and optimise all types of decisions. Those decisions range from day to day, those made via data products & models as well as insight for the bigger strategic and commercial questions. A greater understanding is required about the behaviour of our global digital audience to keep abreast of business strategy and leverage this knowledge to measure performance and evaluate the effectiveness of our digital developments and influence the customer's digital experience. Position Summary We are seeking a proactive and technically strong Senior Analytics Engineer to establish and evolve operational practices that improve the reliability, scalability, observability and maintainability of the FT's data and analytics ecosystem. Initially embedded within the BI Engineering team, the role will focus on improving how data products and pipelines are operated, monitored, supported and continuously improved across their lifecycle. The successful candidate will help create repeatable operational standards, tooling and automation that reduce operational overhead, improve data reliability and enable teams to manage data products more effectively at scale. Working closely with BI Engineering, Data Engineering, Data architecture, Reporting & Analytics and platform teams, the role will identify operational inefficiencies, reliability risks and scaling challenges across the data estate, then design practical technical solutions to address them. The focus of the role is operational enablement and engineering excellence rather than governance ownership. Reporting to the Head of BI Engineering, you will help drive operational maturity across the FT's data landscape through improved observability, automation, incident reduction, lifecycle management and operational insight. You will work across multiple teams to improve how data products are supported and maintained in production environments, ensuring operational considerations are embedded into delivery and engineering practices. The role will establish mechanisms for proactively identifying operational improvement opportunities across pipelines, reporting assets and data products, helping teams prioritise work based on operational impact, reliability, business criticality and engineering efficiency. This role would suit someone who combines strong hands-on technical engineering capability with a highly operational mindset and enjoys solving complex reliability and scalability challenges across distributed data systems and collaborating with stakeholders across multiple disciplines and international teams. The successful candidate will help shape how DataOps evolves within the FT and contribute to the long-term maturity of our data product operating model. Main Duties and Responsibilities Develop and evolve operational practices that improve the reliability, scalability, maintainability and supportability of data products and pipelines, initially within BI Engineering with the potential to expand best practices across the wider analytics ecosystem. Build and maintain monitoring, alerting, observability and operational reporting capabilities to proactively identify failures, degradation, bottlenecks and data quality risks across data products and assets. Design and implement proactive automation that reduces manual operational overhead, improves resilience and increases engineering efficiency before issues impact users or downstream systems. Create and maintain value-based prioritisation approaches that align operational improvement activity to business impact, operational risk reduction and strategic objectives. Work closely with BI Engineering, Data architecture, Reporting & Analytics and wider Data & Analytics teams to embed scalable operational standards and best practices throughout the data product lifecycle. Establish operational lifecycle management processes for data products and assets, including enhancement, optimisation, con

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