BI and Engineering Analyst

• Collate and integrate data from multiple sources across the engineering estate - including Jira, GitHub/Azure DevOps, Datadog, Aha!, HR systems and programme-specific trackers.
• Design and own the metrics framework for the Engineering Transformation Programme, covering engineering productivity, quality, delivery and workforce transition.
• Build and maintain dashboards and reports for programme leadership, divisional VPs, the CTO and the CPO, ensuring they are accurate, timely, and fit for purpose.
• Translate stakeholder questions into data problems and data findings into accessible outputs. Understand what a VP actually needs to see, not just what the data can produce.
• Establish repeatable, low-maintenance data pipelines where possible, reducing the reliance on manual collation and one-off analysis.
• Support the Central VP’s and the Director of Agentic SDLC with data and reporting relevant to their workstreams.
• Flag data quality issues proactively. Where source data is inconsistent, missing, or unreliable, surface that clearly rather than papering over it, ideally with an accompanying solution.

Delivering Trusted Insights

You’ll serve as the analytical backbone of the Engineering Transformation Programme, supporting leaders with accurate and actionable reporting.

You’ll:

• Create executive-level dashboards that drive informed decision-making.
• Build consistent metrics and reporting standards across programme workstreams.
• Develop reporting frameworks that can scale with organisational maturity.
• Ensure stakeholders can trust the integrity and accuracy of the data they rely on.
• Identify trends, risks and opportunities within engineering and workforce data.

Future Growth Areas

• As the programme matures and the supporting data infrastructure evolves, the role is expected to expand into strategic areas including:
• Quality signals as a product input, helping close the loop between Datadog, Pendo and the PDLC so that quality data informs roadmap decisions.
• Self-service reporting capabilities for divisional engineering leaders.
• Contribution to the long-term data strategy for Engineering Operations as the function grows., Our process is designed to be fair, transparent and straightforward. Stages vary depending on the role - more senior positions may involve additional steps, and some areas include role-specific assessments such as a technical test or case study - but typically you can expect: 1. Application review - we assess your experience and potential 2. Initial call - a short conversation to learn about you and share more about the role 3. Skills assessment - tailored to the position, e.g. case study, coding challenge or portfolio review 4. Final interview - meet the team and explore how you’ll make an IMPACT at IRIS

We’ll always walk you through the specific stages at the start of the process so you can prepare with confidence.

Essential Experience:

• Solid background in data analysis and BI, ideally in a software or technology organisation. Comfortable working with engineering, product and operational data.
• Hands-on experience building dashboards and reports in one or more mainstream BI tools - Power BI, Tableau, Looker, or equivalent. Not just maintaining existing dashboards, but designing them from scratch for a specific audience.
• Comfortable with SQL at a working level. Python or similar scripting capability is a plus, particularly for data wrangling and pipeline automation.

Leadership Style & Approach, * Asks the right questions before building. Understands that the stated request and the underlying need are not always the same thing.

• Is a clear communicator who can present findings to non-technical stakeholders without hiding behind data complexity.
• Is self-sufficient and organised, capable of managing multiple requests across different workstreams.
• Is comfortable with ambiguity in source data and surfaces data quality issues honestly rather than producing outputs that look clean but cannot be trusted.

Nice to Have

• Familiarity with engineering metrics frameworks such as DORA metrics, deployment frequency, lead time for change and change failure rate.
• Practical experience integrating data from multiple source systems using APIs, connectors or automated extraction methods.
• Experience working with engineering platforms such as Jira, GitHub, Azure DevOps, Datadog or similar tooling ecosystems.
• Exposure to engineering transformation, software delivery or operational excellence programmes.

IRIS Software Group is one of the UK’s largest privately held software companies, trusted by 100,000+ businesses, schools and accountancy firms to keep their operations running. Our software pays 1 in 6 UK employees, supports over 12,000 schools, and is relied on by 91 of the top 100 UK accountancy firms.

We’re a Great Place to Work® certified employer, recognised for our commitment to well-being, inclusion and development - and we’re growing fast.

Transform Engineering Data into Strategic Decisions

IRIS’s Engineering teams generate a significant volume of data across multiple sources, from engineering metrics and tooling telemetry to workforce data, product telemetry and programme reporting.

Today, much of this data exists in separate systems, is manually consolidated when needed, and does not consistently feed into standardised dashboards that support decision-making across teams, divisions and the executive leadership team.

We’re looking for a highly analytical and commercially minded leader to change that.

This role will establish the reporting, metrics and data foundations that enable the Engineering Transformation Programme to operate with a consistent, evidence-based view of progress. You’ll connect disparate data sources, build trusted reporting frameworks, and deliver insights that genuinely help leaders make better decisions.

This isn’t purely a Data Engineering role, and it isn’t purely a Business Intelligence role. Success requires someone who can move seamlessly between the technical and the practical, understanding the underlying data while delivering outputs that are useful to the people making decisions.

The ideal candidate combines analytical rigour with pragmatism, understanding that a useful dashboard delivered today is often more valuable than a perfect dashboard delivered too late.