Staff Business Analytics Engineer

About Us:

Proofpoint is a global leader in human- and agent-centric cybersecurity. We protect how people, data, and AI agents connect across email, cloud, and collaboration tools. Over 80 of the Fortune 100, 10,000 large enterprises, and millions of smaller organizations trust Proofpoint to stop threats, prevent data loss, and build resilience across their people and AI workflows. Our mission is simple: safeguard the digital world and empower people to work securely and confidently. Join us in our pursuit to defend data and protect people.

How We Work:

At Proofpoint you’ll be part of a global team that breaks barriers to redefine cybersecurity guided by our BRAVE core values:

Bold in how we dream and innovate

Responsive to feedback, challenges and opportunities

Accountable for results and best in class outcomes

Visionary in future focused problem-solving

Exceptional in execution and impact

The Role

Proofpoint is building a single, governed analytical foundation for the company at a moment when it matters most: a Snowflake platform consolidation is underway, a transformative acquisition is signed and pending, and the company is moving toward AI-native analytics. The business definitions already exist and are Finance-approved. What's missing is the durable, auditable, machine-readable layer that lets dashboards, self-service tools, and AI agents all give the same answer. You will build it.

You own the analytical and semantic layer in Snowflake: governed data models, metric logic, and the context that sits between raw data and business consumers. IT and Data Engineering own ingestion and upstream pipelines. You partner with them to establish the analytical requirements, interfaces, validation standards, and data models that enable consistent downstream consumption. You are the technical counterpart to two business-facing pillars and to the Senior Director who owns Proofpoint's ARR/NARR metric definitions. Their recurring questions become your reusable assets. You’ll be expected to understand the business behind the questions, not just the schema behind the data.

This is a senior-level individual contributor role, reporting to the Senior Director, Business Analytics.

Location: This is a hybrid role based in our Sunnyvale, CA HQ, 4 days a week.

Your Responsibilities

Semantic Architecture & Governed Data Assets

Semantic Layer: Design and build governed analytical data models and the semantic layer in Snowflake, translating approved metric definitions (ARR, NARR, retention, customer lifecycle) into reusable, versioned calculation logic.

Metric & Context Registry: Establish a machine-readable metric and context registry so that business definitions, relationships, and calculations are interpreted consistently across Power BI, self-service tools, and AI-driven interfaces. This does not exist today; you will design it.

Acquisition Integration: Integrate acquired businesses into the governed metric layer, including entity mapping, definition reconciliation, and historical restatement.

AI-Native Analytics & Business Intelligence

AI-Native Analytics: Design and implement AI-native analytical capabilities on Snowflake's Cortex ecosystem and governed semantic models, evaluating the architectural approaches that enable reliable natural-language and agent-based access to business information, and recommending how they scale on the platform the company is standardizing on.

Business Intelligence: Evolve Power BI from fragmented functional reporting toward integrated cross-functional views connecting commercial activity, product performance, and financial outcomes.

Data Quality, Governance & Technical Leadership

Reliability Controls: Implement reconciliation, validation, lineage, and monitoring controls that make analytical outputs reproducible, traceable, and suitable for financial reporting governance and audit support.

Technical Standards: Set technical standards and reusable patterns (modeling, dbt usage, testing, documentation) that let analysts and engineering partners build consistently without central bottlenecks.

Upstream Issue Resolution: Identify upstream data quality and architectural issues and drive resolution with system owners.

What you bring to the team

Required

8+ years in analytics engineering, data engineering, BI, or data architecture, with substantial hands-on ownership of production analytical models.

Advanced SQL and deep expertise in dimensional and semantic modeling on a modern cloud data platform.

Experience building enterprise BI and governed self-service reporting at scale.

Experience implementing governed business metrics, reconciliation controls, and consistent analytical definitions across enterprise reporting environments.

Demonstrated habit of starting from the business question: you understand what decision a metric supports, what breaks if it’s wrong, and why the definition is what it is, before you decide how to build it. You don’t need to be a finance expert, but you should be the person who asks.

Track record of leading technical initiatives across teams without formal authority, and of turning ambiguous business requirements into architecture.

Strong written and verbal communication with both technical and finance/business stakeholders.

Preferred

Snowflake and Power BI (our current stack) and dbt.

SaaS or recurring-revenue metrics (ARR, retention, cohorts); the definitions are owned by the team, so you'll be implementing them, not inventing them.

Hands-on work with semantic layers, metric stores, or context layers built for LLM/agent consumption (Cortex Analyst, dbt Semantic Layer, Cube, or similar). This area is new for everyone; we are looking for transferable depth and demonstrated curiosity, not years of experience.

Experience integrating acquired-company data into enterprise models.

Familiarity with CRM, ERP, and financial planning systems as analytical sources.

What success looks like in year one

Core ARR, NARR, and retention metrics are implemented in a governed semantic layer and validated against approved Finance definitions.

Priority reporting and analytical use cases consume shared, tested data assets rather than bespoke extracts.

An initial AI-native analytical capability returns consistent, governed answers to a defined set of business questions.

Integration requirements and mappings for acquired businesses are established, with priority metrics incorporated as integration progresses.

Manual reconciliation and redundant analytical work measurably decline for the supported use cases.

#LI-AN2

Why Proofpoint?

At Proofpoint, we believe that an exceptional career experience includes a comprehensive compensation and benefits package. Here are just a few reasons you’ll love working with us:

Competitive compensation

Comprehensive benefits

Career success on your terms

Flexible work environment

Annual wellness and community outreach days

Always on recognition for your contributions

Global collaboration and networking opportunities

Our Culture:

Our culture is rooted in values that inspire belonging, empower purpose and drive success-every day, for everyone.

We encourage applications from individuals of all backgrounds, experiences, and perspectives. If you need accommodation during the application or interview process, please reach out to accessibility@proofpoint.com .

How to Apply

Interested? Submit your application along with any supporting information- we can’t wait to hear from you!

Consistent with Proofpoint values and applicable law, we provide the following information to promote pay transparency and equity. Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets as set out below. Pay within these ranges varies and depends on job-related knowledge, skills, and experience. The actual offer will be based on th