About the role

Delivery Performance exists to assure the success of the Digital portfolio by strengthening quality and predictability through clear methodology, outcome measurement, and continuous improvement. It builds organisational memory of effective best‑practice standards, improves and simplifies how teams deliver work, and supports programmes with delivery excellence and independent assurance to give transparent oversight of portfolio health and capacity, and address risks before they become issues.

The role - the Delivery Analytics Professional provides the analytical backbone for the Delivery Metrics Programme, transforming raw delivery data into clear, reliable and actionable insight that underpins Digital’s performance measurement framework. The role supports the collection, analysis, interpretation and presentation of data to drive decision making and enable leaders to ‘steer to car’ in the right direction, leveraging automation and AI to bring richer insight and grow confidence in Delivery Performance reporting. The Delivery Analytics Professional creates, validates and continuously refines of standardised delivery metrics, including Earned Value, engine‑room and flow metrics, adoption indicators and balanced scorecard metrics, playing a critical role in ensuring accuracy, data integrity and consistency across all reporting. Working closely with the Metrics Programme Lead, the role interrogates data, identifies trends, highlights anomalies and produces high‑quality analysis and visualisations that help stakeholders understand performance across the Digital Portfolio to ensure the success of the delivery performance function.

Why it’s important – Accurate, trusted measurement is essential for identifying risks, understanding bottlenecks and enabling data‑driven decisions that improve the predictability and effectiveness of Digital delivery. This role ensures that the insights powering those decisions are sound, timely and transparent, strengthening BT’s ability to see what is and isn’t working and intervene early. By elevating the quality, structure and accessibility of delivery analytics, the Data Analytics Professional plays a critical role in improving the flow of value, sharpening portfolio visibility and driving sustained uplift in Digital’s delivery performance.

What you’ll be doing

• Contributes to the build of a new function that establishes transparent visibility of delivery performance and status, to enable early warning and course correction in service of protecting the Digital Portfolio
• Advanced Jira expertise (incl. Advanced Roadmaps and data models) to structure hierarchies, manage cross-portfolio dependencies, and enable consistent, portfolio-level reporting across programmes and initiatives
• Strong capability in Jira reporting using rich filters/JQL, dashboards and standardised data structures to create reliable, real-time insights and establish Jira as a trusted single source of truth for central reporting and governance.
• Sources, extracts, validates, integrates, organises and analyses data, from multiple sources, to enable the functions goals – including input to the management of the
• Digital delivery portfolio, stakeholder dashboards, insight for business decisions, and reporting on key metrics
• Designs, builds and iterates reports and dashboards that intuitively visualise actionable data and insight for stakeholders, maintaining integrity of past trends, and being responsible for their timely and accurate maintenance.
• Leverages metrics and source data to generate actionable insights that mitigates risks or increases efficiency
• Builds a deep understanding of the Delivery Performance Metrics programme, including pitfalls in the meaningfulness of the data and recommends mitigating action to continually increase trust and confidence in the unit’s reporting
• Supports the Metrics Programme Lead in extracting value from our data and innovating new ways of managing and understanding our data, using AI, algorithms and statistical analysis to continually improve management of the Digital Portfolio.
• Drives continuous improvement by automating data collection/production, taking a user-centred approach to uplifting the value of self-serve capabilities, and maintaining documentation and version control on existing data models and reports.[TG1.1]
• Supports the scoping, design, and delivery of data analytics activities focused on performance and continuous improvement across teams
• Continuously explores emerging trends in data analytics and methodologies, sharing knowledge to enhance the team's capabilities and effectiveness. 

Leadership Accountabilities: 

• Growth mindset: The belief that talent, ability and skills can be developed through a commitment to continuous learning and reflection.
• Decision making: Gathers information, and analyses different scenarios, assesses alternative resolutions and reaches a decision.

Essential Skills / Experience

• AI proficiency: Applies AI and automation tools to analyse data at scale, uncover deeper insights, make predictions, and streamline analytical workflows for faster, more accurate decision-making.
• Data Analysis: Collects and interprets data to uncover patterns and trends
• Statistical analysis: Uses mathematical models to quantify relationships between data points and make predictions.
• Data storytelling: Effectively communicates insights from a dataset using narratives and visualisations.
• Programming / scripting: Applies a computer language to communicate with computers using a set of instructions and to automate the execution of tasks.
• Data dashboards: Tracks, analyses, and displays key performance indicators, metrics, and data points
• Business intelligence reporting: Uses a Business Intelligence tool to prepare and analyse data to find and share actionable insights.
• Data design / development: Designs and developments data management systems.
• Data decision support: Models data with the aim of supporting the strategic decision-making process.
• Data quality[TG1.1]: Employs processes, methods, and technologies to ensure the quality of the data meets specific business requirements.

Experience: 

• Previous experience managing measurement frameworks to inform strategic decision making
• Previous experience using data to inform and make business decisions.
• Understanding of the software development lifecycle and delivery methodologies
• Experience working with Qlik, Jira and Planview desirableExperience applying AI in Data Analytics contexts