Vice President, Engineering, EPM

About Us:

insightsoftware is a global provider of reporting, analytics, and performance management solutions that unlock the potential of business data and transform the way finance and data teams operate. We empower leaders from over 32,000 organizations to make timely and intelligent decisions. Our comprehensive solutions span Financial Planning and Analysis (FP&A), Controllership, and Data and Analytics. We deliver finance teams the insights required to navigate any economic climate and drive greater financial intelligence, while increasing productivity, visibility, accuracy, and compliance. Learn more at insightsoftware.com.

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 ru