Senior Analytics Engineer - Operations

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, consolidation, supportability and decommissioning activities.
• Improve operational visibility across pipelines, dependencies, workloads, scheduling and downstream data consumption to support more effective operational decision-making.
• Support scalable operational processes for incident management, issue triage, root cause analysis, remediation tracking and continuous service improvement.
• Contribute to improving data resilience and operational maturity through enhanced tooling, automation, standardisation, documentation and engineering practices.
• Act as a subject matter expert for DataOps and operational excellence, supporting teams in embedding reliability, maintainability and operational best practices into the design and delivery of data products, assets and services.

 

Person Specification (Candidate Profile): 

Essential

• Demonstrable experience improving the reliability, scalability, performance and observability of modern data platforms, pipelines and data products within cloud-based environments.
• Proven experience implementing DataOps, operational engineering, monitoring, observability, or production support practices across data and analytics ecosystems.
• Strong hands-on experience across the end-to-end data lifecycle, including ingestion, transformation, orchestration, reporting, analytics and customer-facing data products.
• Experience building scalable operational processes and automation that reduce manual effort, improve resilience and proactively identify issues before business impact occurs.
• Strong understanding of operational lifecycle management for data products and pipelines, including monitoring, optimisation, supportability, technical debt reduction and decommissioning practices.
• Extensive experience in data engineering, ETL/ELT development, or data platform engineering, with strong SQL and Python skills for developing scalable and maintainable solutions.
• Strong experience with workflow orchestration, pipeline tooling and cloud-based data platforms, preferably including Airflow, AWS and/or GCP technologies.
• Strong understanding of core data and analytics concepts, including data warehousing, data modelling, data quality, lineage, metadata and observability.
• Experience assessing, prioritising and coordinating operational improvement initiatives across multiple engineering teams using operational metrics, service health indicators and business impact measures.
• Ability to design and deliver scalable technical solutions within complex, interconnected data ecosystems, balancing reliability, performance, latency and operational impact.

 

Desirable

These skills are not mandatory in order to apply for this role.

• Experience establishing operational standards, support models, or ops processes for data products and analytics platforms.
• Experience implementing observability tooling, monitoring frameworks, alerting systems, or operational dashboards for data platforms.
• Experience working within a data product operating model or product-led data organisation.
• Working knowledge of technologies such as Redshift, BigQuery, MongoDB, Neo4j, or similar modern data platforms.
• Experience with infrastructure-as-code, CI/CD pipelines, or DevOps tooling.
• Exposure to streaming or real-time data architectures.
• Knowledge of JavaScript, Node.js, or other supporting development languages.
• Experience working in environments where operational ownership, monitoring and production support are embedded within engineering teams.
• Understanding of AI, machine learning, or feature store concepts within modern analytics ecosystems.
• Experience working with modern BI and analytics platforms, preferably Looker and/or Amplitude.

 

Personal Attributes

• Strategic thinker with the ability to balance immediate operational improvements against long-term scalability.
• Naturally inquisitive with a strong problem-solving mindset and a focus on continuous improvement.
• Highly organised with strong attention to detail, operational discipline and commitment to quality.
• Comfortable working in complex, evolving and sometimes ambiguous environments.
• Strong communicator capable of working effectively with both technical and non-technical stakeholders.
• Able to influence teams and stakeholders to drive adoption of operational improvements and prioritisation of DataOps initiatives.
• Comfortable operating across both strategic coordination and hands-on technical delivery.
• Collaborative and relationship-oriented, with the ability to influence across teams and disciplines.
• Proactive and outcome-focused, with the ability to independently identify opportunities for operational improvement.
• Able to manage and prioritise multiple streams of work based on business value, operational impact and strategic importance.
• Passionate about data, analytics, operational excellence and emerging technologies.
• Strong systems-thinking mindset with the ability to understand the wider implications of technical and operational decisions across the data landscape.
• Ability to work in an unstructured and agile environment
• A natural collaborator and team player

 

What’s in it for you?

• Annual bonus scheme
• 25 days paid leave
• 24/7 Employee Assistance Program
• Life Insurance
• Enhanced Parental Leave policy
• Food Allowance
• Multisport Card
• Comprehensive in-house training, and external training programs (subject to approval)

We currently operate a hybrid model which requires staff to work onsite 50% of the time, subject to role requirements & regular review. While flexible working requests will be considered, not all patterns are suitable for all roles. We believe this balanced approach supports flexibility and protects our culture, making collaboration and communication easier, building stronger relationships and team cohesion, and supporting peer learning.  We reserve discretion on reasonable notice to change this approach either generally or for specific individuals or teams. 

 

Our commitment to diversity, equity and inclusion 

We believe in the power of unique perspectives and want all voices in our organisation to be heard, respected and valued. A supportive workplace is one where employees feel they can be themselves and operate to their full potential. We are committed to removing barriers for everyone, with a focus on addressing those faced by underrepresented groups.

 

Accessibility

We are a disability confident employer and Valuable 500 signatory.

Please let us know if you require any reasonable adjustments/personalisation as part of the application process or to enable you to attend an interview. If you would like to discuss your requirements or have any questions, email talent@ft.com and a member of our team will be happy to help.

 

Further information

At the FT, we embrace innovation and the use of technology and appreciate that individuals may leverage AI tools as part of their job application process. Whilst we are happy for you to use AI to assist with your application, it is essential that all information provided is authentic and accurately represents your skills, experience, and qualifications.

Candidates should be aware that the use of AI throughout the application process may be monitored to ensure a fair and transparent hiring process for all.

Please beware of fraudulent job postings and offers claiming to be from the Financial Times. All legitimate opportunities will direct you to apply through the official Financial Times careers site, and the FT will never ask for financial information, payments, or referrals to third parties during the hiring process. If you have any concerns about the legitimacy of a job posting or suspect any scam activity, please contact talent@ft.com. 

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