Director of Product Engineering

Aduro Advisors•Phoenix, AZ

1d•Onsite

About The Position

Aduro is seeking an experienced Director of Product Engineering to lead and grow our product engineering team. This is a people-first leadership role for someone who thrives on building high-performing teams, championing strong engineering culture, and creating the conditions for talented developers — and the AI systems they now work alongside — to do their best work. You will step into a team that is energized and taking real ownership. Our Lead Developers and the broader engineering team are actively contributing leadership and driving outcomes. Your job is to build on that momentum: strengthen the processes, structures, and practices that enable the team to excel, and serve as a strategic partner to leadership on architecture and technical direction — including how we govern and scale AI across the software delivery lifecycle. This role reports directly to the CPTO. You will partner closely with our interim Head of Engineering through an active period of team development, and you will assume durable leadership of the product engineering team as that transition completes. We are looking for a steady, experienced hand who can carry a team through change with clarity and confidence. We're not your typical fund administrators. We're modern, innovative, and backed by deep industry experience — allowing our venture and private equity clients to focus on investing while we handle the details. Engineering sits at the center of that promise: we are building an AI-forward platform for regulated fund-administration data, where auditability, correctness, and data stewardship are first-class concerns. We thrive in a people-first culture where each team member can impact the success of the business. We invest heavily in training and development, provide ample opportunities for growth, and work hard to foster collaboration and community. We believe AI is a powerful accelerator for engineering — and we use it intentionally. We're looking for engineers and leaders who are problem-solvers first : people who can frame the problem clearly, explore options, and use AI to move faster without compromising quality. The differentiating skill we hire for is not tool familiarity — most people can learn the tools — but the judgment to rigorously evaluate AI output and decide what to trust, what to discard, and what to escalate. AI is not "extra credit" here. We expect leaders to operationalize AI across the software delivery lifecycle so the team can continuously retool without sacrificing security, correctness, or maintainability. Use AI to accelerate research, ideation, debugging, test generation, documentation, and refactoring. Embed AI into the SDLC — PR workflows, test strategy, refactors and migrations, incident response, and runbooks — with clear, written standards for what "done" looks like. Treat AI output as a draft. Human judgment and peer review are mandatory, and reviews must explicitly evaluate AI-assisted changes for correctness, security, and architectural fit. Maintain quality guardrails as tools evolve: unit and integration tests, linting and static analysis, CI discipline, and lightweight evaluation and benchmarking where useful. Follow responsible-AI practices: protect sensitive client and fund data, never paste secrets, and use only approved tools and workflows aligned with Aduro's internal AI usage guidelines.

Requirements

10+ years in software engineering, with 6+ years in engineering leadership (Engineering Manager, Senior Engineering Manager, Director, or equivalent).
Demonstrated experience leading through other leaders (Engineering Managers, Lead Developers, Staff+ engineers) and scaling delivery across multiple workstreams.
A strong track record running the full people-leadership cycle: hiring, onboarding, performance management, coaching, and career development and leveling.
Experience building and operating durable engineering processes: planning and estimation, sprint or iteration rituals, prioritization, incident management, and continuous improvement.
Demonstrated experience operationalizing AI across an SDLC at team or organizational scale — not just personal tool use — with defined standards for quality and review.
Judgment for output validation and AI-system governance: reviewing AI-assisted code and architecture for correctness, security, and fit; and defining agent scope, guardrails, and escalation paths.
The ability to engage meaningfully in architectural discussions and technical trade-offs without needing to be a hands-on developer.
Experience guiding teams through transitions — organizational, technical, or cultural — with a calm, structured approach.

Nice To Haves

Experience at a growth-stage or mid-size technology company.
Background in fund administration, fintech, or another regulated / data-sensitive domain.
Familiarity with agentic architectures, eval/benchmarking practices, or AI cost optimization at the system level.

Responsibilities

Coach, mentor, and develop engineers at all levels, with particular focus on empowering Lead Developers and reinforcing a culture of ownership and accountability.
Foster an inclusive, high-trust environment where engineers feel supported, challenged, and invested in the team's success.
Keep the team aligned, motivated, and focused through periods of growth and change.
Evaluate, refine, and build on the processes and structures currently being established with the team.
Drive continuous improvement in how the team plans, ships, and iterates — championing best practices in Agile/Scrum, code review, documentation, and delivery.
Ensure the team has the right tools, workflows, and operational support to work efficiently and sustainably.
Set and maintain architectural direction in partnership with engineers and leadership — engaging substantively in technical trade-offs and validating AI-assisted design decisions, without needing to be a hands-on developer.
Own the governance model for agentic systems — agent scope and escalation paths, what data agents may access, how errors propagate, and the guardrails for security, reliability, testability, and cost — in partnership with the CPTO and security/data governance.
Establish standards for evaluating AI-assisted and AI-driven work: output validation, lightweight eval/benchmarking, and cost considered at the architecture level rather than as an afterthought.
Ensure technical debt is tracked, prioritized, and addressed in a structured way, and help the team make sound build / buy / integrate decisions.
Work closely with Product, Design, Data Engineering, and other stakeholders to align engineering capacity and priorities with business goals.
Translate business requirements into clear engineering objectives; help the team navigate competing priorities with clarity and confidence.
Represent engineering in leadership and planning conversations, advocating for the team's needs and capabilities.
Partner with Talent Acquisition and leadership to grow the team thoughtfully — defining roles, conducting interviews, and onboarding new engineers effectively.
Develop a deliberate team-composition strategy for the AI era that balances senior leverage with a sustainable pipeline of growing engineers — avoiding an inverted, "senior-only" structure that erodes future capacity.
Develop career paths and growth frameworks that reward ownership, sound judgment, and technical excellence.