Who We Are

Having surpassed $300M ARR and continuing to grow, Optro helps enterprises transform risk into opportunity through its AI-powered GRC Intelligence Platform. More than 50% of the Fortune 500, including 7 of the Fortune 10, leverage our award-winning technology to move their businesses forward with greater clarity and agility. And our customers love us: Optro is top-rated on G2.com and Gartner Peer Insights.

At Optro, we inspire each other to innovate and are proud of what we are producing. We spend each day thinking of new ways to help our customers and contribute to the greater good of our company and our surrounding communities. We are all about assisting each other and breaking through barriers to create the most loved audit, risk, ESG, and InfoSec platform by our customers. This is how we have become one of the 500 fastest-growing tech companies in North America for the seventh year in a row, as ranked by Deloitte!

Why This Role is Exciting

Our commercial strategy is only as strong as the data behind it. As a member of the Commercial Strategy & Analytics team, you'll build the data models that power how we measure, understand, and grow our business.

You'll turn data from our revenue systems into trusted, well-documented models that match how the business defines metrics like ARR, bookings, and pipeline. You'll also document how to report on them, so any team pulling its own data gets the same answer. And you'll build the models that help us find growth, including upsell and whitespace potential across our account base, ICP fit, and account scoring.

This is a senior, hands-on individual contributor role for someone who leads like an owner. You'll set the roadmap, keep our core data current as our revenue systems evolve, and balance speed with long-term quality. You'll report directly to the Director of Commercial Strategy & Analytics, on a small team focused on helping commercial leadership make the data-backed calls that drive revenue growth.

Key Responsibilities

Build and own the data foundation

Own the function's roadmap and deliver on it

Build tested, documented dbt models that match how the business defines ARR, bookings, and pipeline

Build analytical models for upsell, whitespace, ICP fit, and account scoring

Build semantic layers that make self-serve easy for analysts and business users

Build AI-powered tools and automations that make the team faster

Set the standard for data architecture, QA, and engineering practices (version control, code review, CI/CD, monitoring)

Govern and document

Own the governance behind our data — clear rules for ownership, access, naming, lineage, and change, grounded in solid data governance practices

Keep documentation current, including how to report on core metrics so every team gets the same answer

Be a strategic partner

Partner directly with stakeholders across Sales, Marketing, and Finance to define problems and scope the right solution

Work with the enterprise data team (hub) and spoke analytics teams on data modeling initiatives. The hub owns ingestion and core infrastructure; you own the commercial modeling layer built on top

Anticipate future analytics needs and proactively invest in them

Represent the team in technical planning conversations

Raise the bar

Evaluate and recommend improvements to our data tooling and infrastructure

Level up analyst’s dbt and modeling skills

Help build a culture where good data and good decisions go hand in hand

Attributes for a Successful Candidate

8+ years in data or analytics roles, with 3+ in analytics engineering, ideally at a fast-paced, data-driven company

A leader's instinct for ambiguity — you set your own direction, know what good looks like, and build it from scratch

Experience building analytical models for GTM teams, such as account scoring, ICP, propensity, or whitespace analysis

Deep, hands-on expertise in data modeling — from raw source data to models people actually want to build on

End-to-end ownership of production data models in Snowflake (or a similar enterprise warehouse), including the trade-off calls between completeness, performance, and speed