Abou the role

Exactera helps large multinationals handle complex tax work: transfer pricing, R&D tax credits, indirect tax, and audit defense. We are moving from a software-and-services model to an AI-native platform that does the work itself, with tax practitioners applying judgment where defensibility requires it.

This role owns the product direction for the agentic platform behind that shift: the system that enables AI agents and practitioner tools to run tax analysis on trusted, current data. The data foundation (a lakehouse, external sources, and the contracts that keep them reliable) is the substrate. The product is what agents and practitioners can do with it.

You work directly with the Principal AI Engineer, who owns the AI/ML architecture, and the Principal Data Engineer, who owns the data tier. You own what the platform does, in what order, and why. Because you define how the company’s practitioners and agents use AI, you have to work deeply in these tools yourself.

Exactera is growth-stage, so the role comes with equity and the room to set the platform’s direction yourself.

What you'll do

The six outcomes that define success in the first 18 months.
O1. Own the product strategy and roadmap for the platform.
You own what the platform is for and what it builds next. The roadmap ties to practitioner outcomes rather than feature counts. When priorities compete across engineering, product, and the practice teams, you decide and keep everyone working from the same plan.

O2. Define what compliance-grade means for this platform.
Tax work has to be defensible. You set the product requirements that make the platform’s output trustworthy: where results must be deterministic, what guardrails constrain agent behavior, how tool access is scoped, and what has to be auditable. You decide where human judgment stays in the loop. This is the bar that regulated tax work requires and general AI tooling does not clear.

O3. Own the agentic access surface.
The platform’s main product is the surface AI agents and practitioner tools use to reach data and act on it. You own that surface: the interface, the contracts behind it, and the limits on what agents can do. How this works decides how the whole platform gets used.

O4. Treat the data foundation as a product.
The data foundation is what the rest depends on, so you manage it as a product. With the Principal Data Engineer, you set the contracts between data producers and consumers: schema, quality, and freshness as commitments practitioners can rely on.

O5. Make external data a platform capability.
The platform is only useful if it reflects what happens outside it: regulatory changes, SEC filings, financial data, third-party sources. You decide which sources matter and how they show up in the product, weighed against their cost. Each is a coverage and value decision.

O6. Measure success by practitioner outcomes.
You measure success by what practitioners can do with the platform: faster, more accurate work with less of it done by hand. A pipeline in production that does not change a practitioner’s day has not succeeded.

What we're looking for

Required

• 10+ years in product management, with significant time on technical platform, data, or ML products. You have owned a platform or major product area at Principal-level scope and driven it across teams without positional authority.
• Able to set product direction across a modern data platform. You understand Lakehouse and medallion patterns, data contracts, quality, and lineage well enough to decide direction and hold credibility with platform engineers, without needing to build it yourself.
• Track record owning AI or ML platform work from an ambiguous problem through to production.
• Strong understanding of agentic AI systems and the data access they require: retrieval, tool calling, MCP or equivalent. You can reason about how agents reach data and what should constrain them.
• Experience with AI systems where correctness and defensibility matter. You have worked on evaluation, guardrails, or human-in-the-loop design, and you understand why a regulated workflow needs more than a capable model.
• You decide what success means for a platform and measure it by practitioner results.
• You communicate clearly across engineering, product, and the practice teams. You hold a high bar without leaning on process, and you work well in ambiguity.
• You use AI tools as part of how you work day to day, and you share what you learn with the people around you.

Preferred

• Experience in tax, financial reporting, regulatory compliance, or another regulated, data-intensive professional services domain.
• Built or governed data products, internal or external, including external-data sourcing and licensing and the trade-offs between coverage, cost, and reliability.
• Hands-on familiarity with Databricks, Delta Lake, or a comparable lakehouse platform in production.
• Background in tech-enabled services or a SaaS-to-services transition like the one Exactera is making.
• Built a platform or major product area from early stage.