BI and Engineering Analyst

Overview

In this role you will establish the data and reporting foundations for the Engineering Transformation Programme, unifying data from multiple sources to deliver trusted, actionable insights. You will design metrics and dashboards that support senior leaders, while building scalable, semi-automated data pipelines. You’ll bridge technical data work with practical, decision-focused outputs for cross-functional stakeholders. This is a hands-on leadership role that values pragmatic delivery and clear communication, shaping how engineering data informs strategic choices.

Responsibilities

• Collate and integrate data from Jira, GitHub/Azure DevOps, Datadog, Aha!, HR systems and trackers
• Design and own the metrics framework for engineering productivity, quality, delivery and workforce transition
• Build and maintain dashboards for programme leadership and executives
• Translate stakeholder questions into data problems and outputs
• Establish repeatable data pipelines to reduce manual analysis
• Support the Central VP’s and Director of Agentic SDLC with relevant data and reporting
• Identify data quality issues and surface them with potential solutions
• Create executive dashboards and ensure consistency of metrics across workstreams
• Develop scalable reporting frameworks that mature with the organisation
• Provide trusted, accurate insights and identify trends and risks in engineering and workforce data
• Future-proof data strategy with self-service reporting and integration of quality signals from tooling
• Contribute to long-term data strategy for Engineering Operations

Key requirements

• Solid background in data analysis and BI in a software/tech environment
• Hands-on dashboard development in Power BI, Tableau, Looker or equivalents
• Working-level SQL skills; Python or similar scripting a plus
• Ability to communicate findings to non-technical stakeholders
• Self-sufficient, organized and capable of managing multiple workstreams
• Comfort with ambiguous data and surfacing data quality issues

• Pragmatic mindset
• Clear communicator
• Questioning mindset (asks the right questions)
• Power BI, Tableau, Looker (or equivalent)
• SQL
• Python (data wrangling and pipelines)

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