Director, Business Intelligence - 11568

The Impact of a Director, Business Intelligence at Coupa:

We are seeking a Head of Business Intelligence to build the reporting and analytics infrastructure for Coupa’s post-sale organization — professional services, customer support, and education. This is a technical, hands-on leadership role: you are the primary architect of our post-sale data engine. Your deliverables are systems, data models, and dashboards — and your success is defined by their quality, reliability, and adoption.

You own the post-sale analytics layer end to end: the function-specific data models, the governed metric definitions, and every dashboard and reporting product the business touches. Coupa’s central data team owns core ingestion pipelines and the enterprise warehouse; you build on that foundation and partner closely with them on shared infrastructure. The ideal candidate brings SaaS post-sale operations experience combined with deep hands-on BI craft — data modeling, transformation, and visualization — and excels at turning business requirements into reporting products leaders adopt.

Reporting to the VP of Value Services Operations, this is a hands-on position. You will personally manage the analytics lifecycle — writing SQL, building models, and shipping the dashboards leaders rely on — working AI-first: leveraging LLM tools at every step to build faster and more iteratively than a traditional BI function. As a team of one initially, you will prove the value of this function and have the opportunity to scale the team as its impact proves out. Success in the first year requires ruthless prioritization of the highest-leverage deliverables.

Infrastructure & Tooling

• Design and build the core reporting architecture: the data models and dashboards for all post-sale functions — professional services (utilization, realization, project margin, backlog), customer support (case volume and mix, backlog and aging, SLA attainment, CSAT, cost-per-resolution), and education (enrollment, completion, certification attainment).

• Automate the reporting cadence: replace manual reporting fire drills with automated, self-service infrastructure — the “single source of truth” dashboards that let leadership run their own QBRs, scorecards, and monthly reviews.

• Build the Customer 360 view: one picture of how each customer experiences Coupa after the sale — support history, services engagements, and education consumption

• Architect capacity forecasting: robust tooling for support volume projections and services staffing models, giving leaders the data to make proactive resourcing decisions.

• Build the unified metric catalog: a governed, self-serve reporting layer so functional teams answer routine questions themselves instead of filing ad-hoc requests.

• Implement proactive alerting: exception reporting that surfaces aging backlogs, at-risk projects, and SLA breaches before they become executive fire drills.

• Measure AI-powered support: create the measurement framework for Coupa’s AI support agents — quantifying true resolution versus deflection, customer experience impact, and cost efficiency — and extend it as AI agents expand across post-sale operations.

Cross-Functional Enablement

• Partner with post-sale leaders and operations: translate business operating questions into a sequenced, technical roadmap of reporting deliverables.

• Drive tool adoption: ensure the tools you build are used — train leaders and teams, document metric definitions, and iterate based on real-world usage.

AI-First Execution

• Develop at speed: use LLMs and AI coding assistants across the entire build — SQL, pipeline development, dashboard prototyping, documentation — to deliver in days what traditionally took weeks.

• Structure unstructured data: apply LLM-based extraction, embeddings, and classification to convert operational noise (tickets, transcripts, notes) into structured, quantifiable signals.

• Iterate constantly: prototype with stakeholders and ship working versions early, prioritizing high-leverage deliverables over big-bang releases.

• Set the standard: continually evaluate emerging AI tooling and define what an AI-native analytics function looks like at Coupa.

Data Quality & Governance

• Build and maintain the post-sale business intelligence data models on the enterprise warehouse, in partnership with Coupa’s central data team, which owns core ingestion and platform infrastructure

• Maintain data quality standards and validation processes across all post-sale reporting and analytics

• Identify opportunities to streamline reporting, improve automation, and enhance usability of analytics tools

• 7+ years in analytics, business intelligence, or operations strategy, with significant experience in a SaaS post-sale organization (professional services, support, customer success, or education)

• Track record of establishing analytics capabilities from scratch — you’ve personally built the reporting and analytical layer of an operational function

• Deep understanding of services economics (utilization, realization, project margin, backlog) and support economics (cost-per-case, SLA design, deflection, capacity planning)

• Expert-level SQL and dimensional data modeling, with hands-on experience in a modern transformation workflow (dbt or equivalent)

• Cloud data warehouse experience (Snowflake, BigQuery, Redshift, or equivalent) and integrating data from systems such as Salesforce, case management, PSA, and LMS platforms

• BI and visualization proficiency (Tableau, Power BI, Looker, or Salesforce CRM Analytics) — building dashboards leaders actually adopt

• Demonstrated AI-native ways of working — you use LLM tools and AI coding assistants daily in your analytics workflow and can show, concretely, how they multiply your speed and output

• Working proficiency with LLM-based text-analysis techniques — extraction, embeddings, and classification — for turning unstructured data (tickets, transcripts, notes) into analyzable signals

• Ability to translate business requirements into reporting products — partnering with senior stakeholders to define the right metrics, then building tools that answer their questions; committed to accurate measurement even when the honest numbers aren’t the popular ones

• Self-directed builder: able to work independently, sequence work effectively, and own projects end-to-end

Preferred Qualifications

• Python or similar for analysis and automation; statistical methods for forecasting and driver analysis

• Active interest in defining the emerging discipline of measuring AI agents in production operational workflows

• Experience with education/customer training analytics (LMS data, certification programs, learning-to-adoption correlation)

• Prior experience supporting senior leaders in management reporting or strategic planning processes

• Ambition to scale the function and grow into broader leadership responsibility after proving its value