Job Description:

As Vice President of Software Engineering for our Enterprise Performance Management (EPM) portfolio, you will lead a global organization of engineers across multiple product lines that finance teams rely on to plan, close, consolidate and report. You will own how that portfolio is built, shipped and run.

2027 is a transformation year. We are modernizing established applications, building new SaaS and multi-tenant capabilities, moving to an agentic software development lifecycle, and putting AI directly into the hands of our customers. We need a leader who can deliver all four at once without compromising the quality and reliability our customers depend on for their most critical financial processes, and who measures success with the same discipline our customers apply to their numbers.

We enjoy our work as much as we enjoy working together, and we want leaders who get things done while having a positive influence on our workplace environment.

What you will deliver in 2027

1. Modernize legacy applications

• Define and execute a modernization strategy for the EPM portfolio, choosing deliberately between re-platform, re-architect, strangler-pattern migration, and retirement for each product and component

• Sequence modernization to deliver customer value incrementally, avoiding big-bang rewrites and protecting customers already in production

• Reduce technical debt measurably, with a visible debt register, investment targets and progress reported to executive leadership

• Plan and execute customer migrations from on-premise and single-tenant deployments with clear data-integrity, cutover and rollback plans

2. Build SaaS and multi-tenant capabilities

• Lead the design and delivery of cloud-native, multi-tenant services, including tenant isolation, data partitioning, configuration and extensibility models

• Build the shared platform capabilities that product teams rely on: identity, observability, metering, tenant provisioning, and self-service environments

• Own SaaS operational excellence: SLOs and error budgets, on-call and incident management, capacity planning, cost efficiency, and disaster recovery

• Embed security, privacy and compliance (for example SOC 2, ISO 27001, GDPR) into architecture and delivery pipelines rather than treating them as downstream gates

3. Deliver with an agentic SDLC

• Lead the shift from AI-assisted coding to an agentic software development lifecycle, where AI agents participate in planning, coding, testing, code review, documentation and operations

• Establish the guardrails that make this safe: human review policy, test and evaluation gates, provenance and traceability of AI-generated changes, and secure handling of code and data

• Redesign team workflows, roles and skills for agentic delivery, and coach engineering managers and engineers through the change

• Measure the impact rigorously, proving gains in flow, quality and reliability rather than tracking adoption alone

4. Build customer-facing AI features

• Partner with Product to deliver AI capabilities that finance users trust, such as natural-language analysis, forecasting assistance, anomaly detection and agentic workflows across planning, close and reporting

• Build the engineering foundations for production AI: model and prompt management, retrieval over governed financial data, evaluation frameworks, and monitoring for accuracy, drift and cost

• Apply responsible-AI practices appropriate to financial data, including explainability, auditability, tenant data isolation, and clear human-in-the-loop controls

• Ship AI features on the same delivery standards as the rest of the portfolio: tested, observable, reversible and measured against defined quality bars

5. Raise the bar on quality and DORA outcomes

• Own engineering outcomes against the DORA metrics (deployment frequency, lead time for changes, change failure rate, failed deployment recovery time, and rework rate), with transparent baselines and quarterly improvement targets for every product line

• Build quality in: test automation across the pyramid, continuous integration on every change, trunk-based development, feature flags, and progressive delivery

• Make reliability a product feature through service-level objectives, error budgets that inform prioritization, and blameless post-incident reviews that lead to lasting fixes

• Shift security left with automated scanning, dependency and vulnerability management, and secure-by-default pipelines

• Report development status, quality, operations and system performance to executive management using a consistent engineering scorecard, and act quickly and decisively to resolve customer-impacting issues

Leadership responsibilities

• Lead multiple software development teams and product lines, including staffing, mentoring, and building high-performing teams across multiple disciplines and geographies

• Collaborate with architects, product managers, software development managers and developers to arrive at the best technical design and approach, delivering value quickly without sacrificing quality

• Drive engineering roadmaps, operational plans and delivery commitments within an Agile/Lean environment, balancing modernization, new capability and operational health

• Manage departmental budget and resources, including the mix of full-time employees, contractors and suppliers, and negotiate contracts and SOWs with vendors

• Build an engineering culture of ownership, psychological safety, continuous improvement and customer focus, where teams own what they build in production

• Evolve the software development practice across the organization, including practices, tooling, reporting and methodology

Competencies

• Proven delivery leader - Brings deep software development and operational management experience and shows what good looks like in practice

• Transformation leadership - Leads large-scale technical and organizational change while keeping the business running

• Quality and reliability mindset - Treats quality, security and reliability as non-negotiable outcomes, and knows how to engineer them into the system

• Data-driven decision making - Uses metrics to understand flow and outcomes, and to guide investment, not to police teams

• Leadership and vision - Inspires people at all levels to follow a clear technical and operational vision

• Planning and management - Highly effective planning, organizational and operational skills

• Discipline and perseverance - Commitment to solving complex issues through to completion

• Adaptability - Operates in a fast-paced, iterative environment; learns and adapts to new business demands and fast-changing AI technologies

• Problem solving - Strong critical thinking and problem-solving capabilities

• Effective communicator - Excellent written, presentation and oral communication at both executive and team level

• Prioritization - Uses a sense of urgency to prioritize effectively and manage time well

Qualifications and experience

Required

• BS in Computer Science, Computer Engineering, or a related technical discipline

• Prior experience serving as a Vice President of Engineering, with 10+ years of progressive engineering leadership experience

• 10+ years of software development experience, with at least five years building and running cloud products using Java, C#, and JavaScript/TypeScript

• 7+ years leading multiple Agile teams and programs, with expert understanding of Agile and Lean principles

• 5+ years driving full DevOps and continuous delivery practices, with a demonstrated track record of improving DORA metrics and software quality at scale

• 3+ years managing global engineering teams and suppliers across multiple products, including teams built from a combination of FTE and contractor resourcing

• Proven experience leading the modernization of legacy or on-premise enterprise applications to cloud-native SaaS, including customer migrations

• Hands-on leadership experience with multi-tenant SaaS architecture and operational production responsibility, including SLOs, incident management and on-call

• Demonstrated experience using AI and agentic tooling to measurably improve the productivity and quality of engineering teams across a product portfolio

• Proven ability to manage multiple product lines and meet release commitments

• Experience negotiating contracts and SOWs with vendors

Preferred

• Master's degree in a technical or business discipline

• Experience shipping customer-facing AI or machine learning features to production, including LLM-based capabilities, retrieval-augmented generation and evaluation frameworks

• Experience with EPM, FP&A, financial close or other finance applications where accuracy and auditability are critical

• Experience operating under enterprise security and compliance frameworks such as SOC 2, ISO 27001 and GDPR

• Familiarity with platform engineering and internal developer platforms

• Broad experience across application platforms, middleware, frameworks, cloud providers (AWS, Azure) and programming languages

The salary range in United States of America for this position is 212,000.00 to 265,000.00 USD Annual.

For sales roles, this range includes the commission target. For non-sales roles, this is a base salary only range; additional bonus eligibility may apply.

Your specific offer within this range will be determined by your skills, experience, and qualifications.

We are committed to pay transparency and fair compensation practices. If you have questions about our compensation approach, please don't hesitate to ask during the interview process.

Additional Information

All your information will be kept confidential according to EEO guidelines.

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