Director, Business Intelligence & Analytics

We are seeking a Director, Business Intelligence & Analytics (BIA) to report to the Vice President of Revenue Operations. This is a builder's role for a leader who thinks in systems, treats analytics like a product, and is excited to operate at the frontier of AI-augmented decision-making. A leader who can define what we measure, why it matters, and how every number ties back to a clean, trusted source. Before anything else, this person establishes the foundation the rest of the business runs on: a rigorous set of KPIs, unambiguous metric definitions, sound data models, and a single version of the truth that executives and sellers alike can rely on. Get that right, and everything else compounds. Get it wrong, and no tool or technique can save it.

You will own the operational backbone of Revenue Operations analytics — the metrics, the definitions, the governance, and the reporting that leadership uses to run the business. You will gather requirements across teams, reconcile conflicting numbers, close data gaps, and turn fuzzy questions into precise, durable measures. This is the work that makes everything else possible.

With that foundation in place, you will modernize how we deliver it. We are migrating from a legacy Redshift/Alteryx/Tableau stack to a modern, code-driven ecosystem built on Redshift, dbt, Dagster, and Airbyte, paired with a next-generation, version-controlled BI layer. The goal is to treat analytics like a well-engineered software product — tested, documented, version-controlled, and built to scale as fast as the business does.

This modernization unlocks the next chapter: an AI-augmented function where a strong operational core is amplified by emerging tooling. We believe the analytics team of the next few years will increasingly work from an AI coding cockpit — agentic developer tools like Claude Code and Codex alongside GitHub — to build, review, and ship pipelines, models, and dashboards. On top of well-defined metrics, that opens the door to conversational analytics, AI-generated narratives, and agents that surface and act on insight. The key word is on top of — AI multiplies a solid foundation; it does not substitute for one. The ideal candidate has already moved past the chat window and uses agentic coding assistants to do real engineering work, but reaches for them in service of operational rigor, not in place of it.

As the subject matter expert for Business Intelligence and Analytics, you will partner closely with field and operational teams, instituting leading practices that pair analytics-engineering discipline (Git, CI/CD, testing, semantic layers) with self-service access. You will lead a team of Business Intelligence & Analytics Analysts, Analytics/Data Engineers, and Data Quality Specialists.

Above all, this is a full-stack, player-coach role — not a hands-off manager. You'll lead a team, but you'll also be in the work: your morning might be deep in data engineering, pairing with Claude Code to refactor a dbt model or debug a Dagster pipeline, and your afternoon might be building and delivering a polished, board-ready presentation that translates that same data into a clear story for the C-suite. We're looking for someone who is equally credible in a terminal and in the boardroom, and who is genuinely energized by the full range — from the plumbing to the podium.

Key Responsibilities:

Operational Excellence & Metric Definition (the foundation)

• Own and maintain a comprehensive, well-governed set of business KPIs and the precise metric definitions behind them — the trusted source the rest of the business runs on. This spans the core recurring-revenue metrics this function lives and dies by — ARR, ACV, GRR, NRR, churn and contraction, bookings, pipeline and pipeline coverage, win rate, sales-cycle length, quota attainment, and forecast accuracy — along with the upstream operational measures that feed them.
• Nail the definitions where the traps live: how contraction is treated across GRR vs. NRR, churn vs. downgrade, ACV vs. TCV, new vs. expansion vs. renewal, and how each metric is segmented and rolled up. Make the calculation logic explicit, documented, and consistent everywhere it appears.
• Establish a "single version of the truth": reconcile conflicting numbers, eliminate ambiguity in how metrics are calculated, and ensure every figure traces back to a clean, documented source.
• Gather requirements across teams, identify and close data gaps, and turn fuzzy business questions into durable, precise measures.
• Design and document the analytics/data model for key personas across Turnitin — sellers, sales management, executives, and operations staff.
• Audit and ensure the cleanliness, completeness, and reliability of data through automated testing and validation.
• Partner with the Business Planning & Operations group to co-develop and continuously improve reporting and analysis, including preparation for Quarterly Business Reviews.
• Build automated, repeatable reporting solutions rather than one-off manual reports.

Strategic Leadership

• Develop and execute a BI strategy aligned with company objectives, anchored in operational rigor and trusted metrics, and built to scale with business growth.
• Establish the architecture, standards, and governance that keep data trustworthy as the function and the business scale.
• Shape the long-term vision for the analytics function and the roadmap that takes the team from today's stack to a modern, code-driven, and ultimately AI-augmented operating model.
• Provide thought leadership and drive innovation across the enterprise analytics portfolio.

Modern Data Platform & BI-as-Code (how we deliver the foundation)

• Lead the migration from the legacy stack (Redshift, Alteryx, Tableau) to a modern ecosystem: dbt for transformation and modeling, Dagster for orchestration, Airbyte for ingestion, Redshift as the warehouse, and a modern code-first BI/semantic layer.
• Treat BI as a software product — version control everything in Git, with code review, testing, CI/CD, and documentation as the default way of working.
• Stand up and govern a code-driven semantic layer that turns the metric definitions above into reusable, testable, single-source assets, replacing brittle GUI-built reporting.
• Drive data quality, governance, security, and access controls as code, with automated validation and monitoring.
• Develop intuitive self-service dashboards that support global requirements.

AI & Agentic Analytics (the amplifier, built on the foundation)

• Champion the adoption of agentic coding tools (e.g., Claude Code, Codex) across the team for pipeline development, model building, dashboarding, and analysis — moving well beyond chat-window prompting.
• Build conversational analytics experiences that let stakeholders query data in natural language and receive trustworthy, governed answers — only ever on top of well-defined metrics.
• Implement AI-generated narratives that automatically explain "what happened and why" on top of dashboards and KPIs.
• Pilot and operationalize AI agents that don't just surface insight but take action on it — drafting analyses, opening pull requests, flagging anomalies, and proposing next steps.
• Stay ahead of the rapidly evolving LLM and agent tooling landscape, and translate it into practical productivity gains for Revenue Operations.

Collaboration and Communication

• Work closely with business users, stakeholders, and the broader Go-To-Market and Revenue Operations teams to translate business needs into analytics initiatives.
• Communicate effectively across technical and non-technical audiences and across geographies.
• Present complex analyses and insights to non-technical stakeholders in a clear, actionable manner.

Team Development

• Lead as a player-coach — set direction and mentor the team, while staying hands-on in the build, modeling the engineering and presentation standards you expect. 
• Build, mentor, and manage a high-performing team of analysts, analytics/data engineers, and BI developers.
• Upskill the team in analytics engineering practices and AI-assisted development.
• Foster a culture of innovation, continuous improvement, and data-driven decision-making.