Head of BI Platforms and DataOps (SEL & SWL)

We are looking for an accomplished technical leader who can operate comfortably across technology, people, operations and delivery while understanding how their area contributes to wider strategic priorities.

You will have sufficient technical depth to understand complex data architecture and engineering, interrogate proposed solutions and constructively challenge technical approaches while maintaining sight of the organisational and analytical problems that technology is intended to solve.

You will understand how data pipelines, orchestration, transformation logic, analytical data models, cloud platforms and software engineering disciplines combine to create reliable analytical services.

You will also understand what is required to move technical solutions from development into stable production operationincluding CI/CD, version control, automated testing, monitoring, change control, reusable code, common data models and MLOps.

Crucially, we are looking for someone who can provide genuine technical leadership rather than simply manage technical specialists .

You will be comfortable operating across organisational levelsleading specialist technical teams, working collaboratively with analytical and operational colleagues, and providing clear technical advice, assurance and recommendations to Deputy Directors and senior leaders.

You will be pragmatic, collaborative and inclusive, able to create an environment in which Data Engineering, Data Science, DataOps and Analytics operate as interconnected capabilities rather than isolated technical disciplines.

What makes the role different?

The defining proposition of this role is to translate data strategy into operational capability .

You will take strategic priorities and turn them into effective technical services, engineering standards, delivery plans and operational practices that enable data and analytics to move efficiently and reliably from source to insight.

This means looking beyond individual technologies and technical products to understand how the overall service performs: where processes are unnecessarily manual, where technical debt constrains delivery, where engineering patterns should be standardised, where greater reuse is possible and where automation can release analytical capacity.

Success will not therefore be measured simply by the number of pipelines, models or technical products delivered. It will be demonstrated through measurable improvements in the performance and effectiveness of the analytical function .

What will you do?

Provide operational and technical leadership of the DataOps capability translate strategic priorities into effective technical services, operating practices, standards and delivery plans, ensuring the capability is reliable, responsive and aligned with the needs of the wider analytical function.

Provide technical leadership and assurance across architecture, engineering and analytical platforms challenge technical approaches, guide architectural decisions and ensure solutions are appropriately designed for performance, resilience, security, maintainability, scalability and reuse.

Lead data orchestration, transformation and production engineering establish robust and consistent approaches to ingestion, ETL/ELT, transformation, analytical data modelling, deployment, monitoring and production operation.

Drive automation and productivity across DataOps and the wider analytical function identify manual, repetitive and inefficient processes and use engineering, Data Science and automation to release capacity and accelerate analytical delivery.

Lead the development and optimisation of Microsoft Fabric, Snowflake and Azure capabilities ensure platform architecture, performance, capacity, scalability, resilience and cost are actively managed and aligned with analytical requirements.

Establish and assure modern DataOps, DevOps and MLOps disciplines embed version control, automated testing, CI/CD, monitoring, deployment pipelines, reusable code, change control and appropriate model deployment and monitoring practices..

Demonstrating measurable benefit

We want to see evidence that the DataOps capability is making the wider analytical function more effective.

Success could include reducing the time analysts spend acquiring, cleaning and transforming data; automating repetitive analytical and operational processes; reducing pipeline failures and manual interventions; improving data quality and timeliness; increasing reuse of curated datasets, transformation logic, models and code; reducing the time required to move analytical and Data Science products into production; improving cloud performance and cost-effectiveness; and releasing analytical capacity for higher-value work.

You will establish the measures required to demonstrate these benefits and use them to inform operational priorities, continuous improvement and recommendations to senior leadership.

The question we want the capability continually to ask is: How can technology, engineering and automation make our analytical function more effective?

Why join us?

You will lead a multidisciplinary technical capability operating at the intersection of Data Engineering, Data Science and Analytics across South East and South West London, supporting teams working with complex health and care data.

The role provides the opportunity to translate strategic priorities into practical technical capability while remaining sufficiently close to delivery to understand how architecture, engineering, platforms, automation and operational decisions affect analytical services.

You will work with senior leaders, technical specialists and analytical teams, giving you the opportunity to influence technical direction, improve service performance and establish stronger connections between technology investment and analytical value.

You will be joining at an important stage in the development of our data environment, with the opportunity to help establish a mature, integrated and measurable DataOps capability that enables better analytical delivery and decision-making .

If you are a technically credible leader who combines operational leadership, analytical judgement, strong people leadership and a determination to turn strategic priorities into measurable technical and analytical capability, we would like to hear from you.

Lead, operationalise and continuously improve the DataOps function provide strategic, technical and operational leadership across Data Engineering, Data Science and DataOps, establishing a high-performing service that demonstrably improves the speed, quality and reliability of analytical delivery.

Provide technical leadership for data architecture, engineering and analytical model development set and assure standards for data structures, transformation logic, common data models, code, analytical models and production solutions, ensuring they are robust, scalable, reusable and maintainable. The JD specifically places responsibility for architecture and operation of the platforms with this role.

Lead data orchestration, transformation and production engineering design and optimise automated ETL/ELT pipelines covering ingestion, wrangling, cleansing, transformation, validation, modelling and delivery, reducing unnecessary manual intervention and improving the flow of data from source to insight.

Drive automation and measurable productivity improvement across the wider Insights function proactively identify repetitive, inefficient or manually intensive processes that can be automated or redesigned, releasing analyst capacity for higher-value analytical work.

Lead the architecture, operation and optimisation of Microsoft Fabric, Snowflake and associated cloud technologies ensuring platforms are secure, performant, resilient, appropriately governed and cost-effective, including active management of platform capacity and consumption. This reflects the JD's explicit responsibility for both platforms, including budget, resource and performance management.

Establish mature DataOps, DevOps and MLOps practices implement CI/CD, version control, automated testing, monitoring, deployment and change-control processes, and create the environments necessary to move Data Science and Machine Learning models reliably from experimentation into production.

Please see job description for full detail of main responsibilities.