Process & Tooling Engineer

At Regnology our greatest asset is our people. We value diversity and foster an inclusive environment that supports learning, safety, and growth. By investing in our team, listening to customers, and encouraging creativity, we drive continuous innovation and improvement.
We offer a flexible, innovative environment where your ideas matter and your career can thrive.
Regnology is a global leader in regulatory reporting, helping financial institutions, regulators, and tax authorities improve data quality, efficiency, and cost savings. Our mission: to safeguard financial markets through smarter, future-ready solutions.
What you'll do
You will make Regnology's Research & Development (R&D) organisation more efficient by automating work that is still done by hand, keeping our Atlassian Cloud toolchain healthy, and turning AI into practical time saved for engineers and Product Management (PM).
This is not a pure Jira admin seat. A large share of the job is building and maintaining automation — scripts, integrations, workflow automation and AI-assisted flows — and judging when a simpler configuration change or a deleted process step beats another tool.
Our platform is Atlassian Cloud, above all Jira Cloud and Confluence Cloud. Experience with Jira Server or Data Center is useful background, but this role owns Cloud: native configuration, Cloud automation, APIs and marketplace or Forge extensions where they fit. Custom plugin habits from Data Center are not the default answer here.
You will also manage external delivery: scope and order work, plan what others must do, accept completed work, and test it before it lands in production for R&D.
Responsibilities
Automation & AI (core of the role)
Identify repetitive, manual or error-prone work in R&D and PM workflows and remove it, prioritising hours returned to teams over technical novelty.
Design, build and maintain automation across the toolchain — primarily in Python — including scripts, scheduled jobs, webhooks and integrations between SDLC tools.
Build AI-assisted workflows (for example summarisation, drafting, triage or retrieval over our own documentation and issue history) with a clear measure of whether the output is good enough to trust.
Apply judgement where AI is the wrong tool and a deterministic script, a configuration change or deleting a process step is the better answer.
Keep automation maintainable: version-controlled, reviewed, documented and observable.
Jira Cloud & Atlassian suite
Serve as a key contact for Jira Cloud and other Atlassian Cloud products (for example Confluence), including workflows, schemes, fields, permissions, dashboards and native automation.
Extend the platform beyond out-of-the-box behaviour only where warranted (marketplace apps, Forge or services alongside Cloud) — not by defaulting to heavy customisation.
Provide user support and guidance, and build documentation and enablement that reduce how often that support is needed.
External delivery management (order, plan, accept, test)
Brief and order work from external suppliers (Atlassian partners, integration or automation partners, and similar) so that scope, acceptance criteria and timelines are clear.
Plan and break down work for others — external and internal contributors — including sequencing, dependencies and who owns what.
Accept delivered work against agreed criteria; test configuration, automation and integrations before they are released to R&D or PM.
Chase quality and completeness; push back when delivery is incomplete, undocumented or will create long-term maintenance cost on the Cloud platform.
Process & tooling improvement
Analyse and document R&D and PM processes to find what can be simplified, standardised or automated — including concluding that a process should be removed rather than tooled.
Support Agile ways of working (for example Scrum, Kanban) tailored to business needs.
Contribute to continuous improvement of the software development lifecycle (SDLC), from requirements through delivery.
Collaboration & M&A integration
Work with R&D, Product Management and other stakeholders to understand needs and translate them into tooling and process that get adopted — starting from the problem, not from a pre-chosen solution.
Act as a bridge between R&D and PM so processes, tools and operational needs stay aligned.
Support onboarding, training and adoption of tooling and process improvements.
Support integration of R&D processes, systems and toolchains during mergers and acquisitions where required.
Why we should decide on you
Essential
Hands-on experience with Jira Cloud administration and configuration (workflows, schemes, permissions, automations, fields). Server/Data Center-only backgrounds without Cloud ownership are not enough for this role.
Demonstrable experience automating real workflows end to end (scripting preferably in Python, plus integrations between tools) — not only clicking configuration screens.
Practical understanding of AI applied to real workflows (something built or shipped, not only interest), and judgement about when not to use it.
Experience coordinating external delivery: ordering or briefing suppliers, planning work for others, accepting and testing what comes back.
Good understanding of SDLC and Agile delivery from having worked inside them.
Hands-on experience with the adoption of tools or process changes, including enablement and follow-through, not a change-management certificate.
Ability to explain technical and process topics clearly to mixed audiences, and to push back constructively when a requested exception would damage the platform.
Familiarity with AI concepts and its potential application in a b