Director, Business Intelligence & Analytics (BIA) - USA Remote

Turnitin, LLC•Dallas, TX

7h•$130,350 - $217,250•Remote

About The Position

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. 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.

Requirements

Minimum of 10 years in business intelligence, data analytics, analytics engineering, or related fields, with at least 5 years in a leadership role.
7+ years across business/revenue operations, business analytics, consulting, and/or information technology.
Demonstrated track record defining KPIs and metric frameworks, and establishing a trusted single source of truth across an organization.
Deep fluency in SaaS recurring-revenue metrics — ARR, ACV, GRR, NRR, churn/contraction, bookings, pipeline, win rate, quota attainment, and forecast accuracy — including the judgment to define them rigorously and defend the calculation logic.
Proven ability to source and integrate data from multiple systems into a single, trusted view, and to reconcile conflicting numbers.
Practical end-to-end experience across requirements, design, development, testing, and deployment of analytics solutions at varying scale.
Strong SQL skills, plus working proficiency in Python for data work and automation.
Experience with BI and visualization tools (e.g., Tableau today, with a clear point of view on code-first BI tools) and with CRM data for revenue reporting and forecasting (e.g., Salesforce).
Proven track record implementing and managing self-service BI solutions.
Experience analyzing complex data relationships and exploring "what-if" scenarios.
Hands-on experience with a modern, code-driven data stack — including transformation (dbt), orchestration (Dagster or similar), and ingestion/ELT (Airbyte or similar) — on a cloud warehouse such as Redshift.
Comfort working in a version-controlled, code-first environment (Git/GitHub), including code review and CI/CD for analytics.
Demonstrated, hands-on use of LLMs and agentic coding tools (e.g., Claude Code, Codex) for real engineering and analytics work — not just chat-based prompting.
A systems thinker — able to see how metrics, data sources, processes, and teams connect, and to design analytics that hold together as the whole system changes.
Strong analytical skills — able to evaluate information from multiple sources, reconcile conflicts, decompose high-level requests into detail, and abstract detail into general understanding.
Demonstrated business acumen and the ability to apply technology to solve business problems.
Self-starter who operates independently with a track record of success on strategic and operational work.
Ability to lead teams across functions and geographies on ambiguous, complex problems.
A full-stack player-coach — hands-on enough to build the pipeline yourself, polished enough to present the result to the C-suite, and able to move between the two in the same day.
Strong written and verbal communication skills, with a track record of presenting to senior management.
Ability to manage multiple competing priorities and drive projects to completion.
Sound business judgment, a proven ability to influence, and a bias for ownership.
Bachelor's degree in Business, Data Science, Computer Science, or a related field.

Nice To Haves

A Master's degree or MBA is preferred.
Experience supporting education and/or nonprofit institutions.
Management or Financial Consulting background.
Experience building conversational analytics, AI-generated narratives, or semantic/metrics layers.
Experience deploying AI agents that build pipelines, dashboards, or analyses, or that act on insights.
Familiarity with data quality and observability tooling, and with governance-as-code.
Advanced degrees, certifications, or coursework in business intelligence, analytics, data science, or AI/ML.

Responsibilities

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.
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.
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.
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.
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.
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.