Job Description:

Mission:

Build and lead a small, exceptional, AI-native engineering organisation that turns real customer problems into scalable, reliable and cost-effective software — while developing the next generation of engineering leaders and continuously improving how Storyful ships.

Why this role exists

Software engineering is changing rapidly. Agentic development has removed many of the old constraints around writing code, but it has also shifted the bottlenecks into planning, verification, review, quality, security and decision-making.

We want a leader who has already made that transition. You will lead an organisation of approximately 18 people through Tech, our QA Automation Lead, Infrastructure Lead and Principal Engineer. Your job is to create the conditions in which talented people do their best work: clear outcomes, strong technical judgement, high trust, low ego, fast feedback and just enough process.

This is not a role for a manager who has moved away from engineering. You will lead from the front — coaching leaders, challenging architecture, joining incidents, reviewing code and occasionally prototyping or shipping with Claude when that is the highest-leverage thing you can do.

What you’ll do (Responsibilities)

1) Build leaders and high-performing teams

• Lead, coach and develop 5 -7 senior technical leaders, creating clear growth plans, candid feedback loops and meaningful succession paths.
• Build a track record of promotions and expanded leadership scope by developing the leaders of tomorrow — not by becoming the decision-maker for everything.
• Set a high bar for performance, ownership and collaboration; address under-performance early and fairly, and recognise exceptional impact.
• Create a culture of humility, curiosity, challenge and psychological safety where strong people can disagree well and then commit.

2) Create an operating model for speed without bureaucracy

• Design and continuously improve a lightweight engineering operating model: clear accountabilities, fast decisions, small batches, short feedback loops and minimal ceremony.
• Remove process that no longer adds value. Encourage teams to prototype, test assumptions quickly and fail fast in discovery — while maintaining a high bar for production quality and customer trust.
• Give teams autonomy inside clear guardrails, making ownership and decision rights obvious across Engineering, QA Automation and Infrastructure.
• Use delivery and quality data to find bottlenecks and improve the system rather than using metrics as targets in themselves.

3) Build an AI-native engineering operating system

• Make Claude and Claude Code a standard part of how the organisation discovers, designs, builds, tests, reviews, documents and operates software.
• Codify an AI operating system that includes shared context, reusable skills, deterministic guardrails, permissions/hooks, review standards, testing practices and appropriate governance.
• Coach leaders and engineers on effective agentic development, context engineering and critical review of AI-generated work so speed never comes at the cost of engineering judgement.
• Measure whether AI adoption is actually improving outcomes — including cycle time, onboarding/ramp time, quality, reliability and the speed at which customer problems are solved.

4) Own engineering delivery against customer and business outcomes

• Partner closely with Product and business leaders to translate customer pain points and company priorities into clear engineering outcomes.
• Keep teams focused on the problem being solved, not the volume of tickets completed. Challenge scope, sequence work intelligently and create momentum through ambiguity.
• Build tight feedback loops between customers, Product and Engineering so teams can learn quickly from what is actually happening in the market.
• Be accountable for predictable delivery while preserving the ability to change direction when evidence says we should.

5) Set the technical bar for scalable, reliable and cost-effective software

• Provide technical leadership across architecture, scalability, reliability, security, observability, infrastructure, quality automation and cloud cost.
• Be comfortable going deep with technical audiences: lead design reviews, challenge trade-offs, review production code, debug difficult problems and make build-versus-buy or sequencing decisions.
• Ensure quality is engineered into the delivery lifecycle through strong automation, CI/CD, observability, incident learning and pragmatic engineering standards.
• Work closely with the Principal Engineer and technical leads so architecture evolves deliberately without creating unnecessary centralised decision-making.

6) Lead by example

• Stay close enough to the work to understand the reality of the teams you lead. Prototype with Claude, review code and get involved when your technical judgement can materially unblock progress.
• Communicate technical strategy clearly to engineers and business strategy clearly to executives, connecting architecture choices to customer value, risk, cost and growth.
• Bring energy, urgency and optimism without creating chaos. Be the person who raises standards, creates clarity and makes the people around them better.

What success looks like (first 6–12 months)

• A stronger leadership bench: every lead has clear expectations and development goals, and there is visible evidence of people growing into broader responsibility.
• A simple, understood engineering operating model that improves speed, collaboration and accountability without adding bureaucracy.
• Claude Code is embedded as an organisational capability rather than an individual productivity tool, with reusable skills, guardrails, review practices and measurable improvements in delivery and quality.
• Engineering teams are demonstrably more autonomous and consistently connect technical work to customer and business outcomes.
• Reliability, quality, infrastructure and cloud-cost practices are measurable, owned and improving as the platform scales.
• The team have a trusted engineering leader who can move fluently between strategy, people, architecture and execution.

Required experience (Must-have)

• Proven engineering leadership experience in a high-growth or scale-up SaaS product business, including recent experience managing Tech Leads, Engineering Managers or equivalent senior technical leaders.
• A demonstrable track record of developing people and leaders: promotions, expanded scope, strong succession pipelines and teams that became more capable under your leadership.
• Strong software engineering foundations and enough recent hands-on experience to review production code, contribute to architecture, prototype and debug credibly.
• Hands-on Claude / Claude Code experience is a hard requirement. Over the last two years you have led teams that moved beyond experimentation and became demonstrably AI mature.
• Evidence that you personally changed an engineering operating model around AI: improving throughput and/or quality, teaching teams how to work effectively with AI, and establishing guardrails, skills, processes and governance.
• Experience leading across product engineering plus disciplines such as Infrastructure / SRE / Platform and QA Automation, with a strong understanding of reliability, observability, CI/CD, testing and cloud economics.
• A track record of building scalable, reliable, secure and cost-effective software that solves real customer problems — not technology for technology’s sake.
• Strong product and customer instincts, with examples of engineering decisions driven by customer evidence and measurable business outcomes.
• Excellent communication and influence: comfortable debating architecture with senior engineers and discussing strategy, trade-offs and investment with executives.
• High agency, humility and sound judgement in ambiguous environments. You know when to delegate, when to challenge and when to get directly involved.

Nice-to-have (Strong bonuses)

• Experience building data-intensive, AI-powered, media, intelligence or real-time information products.
• Experience scaling an engineering organisation through a meaningful growth or transformation phase.
• Deep experience in one or more of: distributed systems, cloud infrastructure, platform engineering, reliability engineering, developer productivity or automated quality engineering.
• Experience defining engineering career frameworks, leadership expectations or succession programmes.
• Experience using AI to improve more than coding — for example incident response, documentation, testing, architecture review, developer onboarding or engineering analytics.

Skills & tools (examples)

We do not require an exact technology match. We do expect you to be technically fluent, able to learn quickly and able to choose tools pragmatically.

AI-native engineering

• Claude / Claude Code
• Shared project context and instructions
• Skills, hooks, permissions and guardrails
• AI-assisted code review, testing, debugging and prototyping

Engineering systems

• Cloud architecture (AWS) and cost optimisation
• CI/CD and infrastructure as code
• Observability, SLOs and incident management
• Automated testing and quality engineering

Technical fluency

• Modern backend and web architectures
• Distributed / event-driven systems and APIs
• Security and reliability fundamentals
• Ability to read, review and occasionally ship production code

Equal Opportunity Employer

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status or any other protected characteristic under applicable law.

Reasonable Accommodation

We are committed to providing reasonable accommodation for qualified individuals with disabilities in our job application and/or interview process. If you need assistance or accommodation in completing your application or participating in an interview due to a disability, email us at talentresourceteam@dowjones.com. Please put "Reasonable Accommodation" in the subject line and provide a brief description of the type of assistance you need. This inbox will not be monitored for application status updates.

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Business Area:

Dow Jones - Risk

Job Category:

Software Product Engineering

Union Status:

Non-Union role

 

Base Pay Range: €140,000 - €155,000

We’re committed to offering competitive and flexible compensation to attract top talent. This pay range reflects our good faith estimate for the role and may vary based on a candidate’s experience, skills, location, and other relevant factors.

For bonus-eligible roles, targets are determined based on multiple considerations, including market benchmarks and individual contributions.

For benefits-eligible roles, we offer a comprehensive and competitive benefits package covering health, retirement, wellbeing, and more, along with optional benefits to meet the diverse needs of our employees.