Staff Business Analytics Engineer

Proofpoint•Sunnyvale, CA

2d•$124,100 - $244,200•Hybrid

About The Position

Proofpoint is building a governed analytical foundation during a Snowflake platform consolidation, a pending acquisition, and a transition toward AI-native analytics. The Staff Business Analytics Engineer will own the analytical and semantic layer in Snowflake, building durable, auditable, machine-readable data models and metric logic that enable dashboards, self-service tools, and AI agents to provide consistent answers. Approved business definitions already exist, including ARR and NARR definitions owned by the Senior Director; the role will implement those definitions rather than invent them. This senior-level individual contributor partners with IT and Data Engineering, who own ingestion and upstream pipelines, to establish analytical requirements, interfaces, validation standards, and data models for downstream consumption. The role also serves as a technical counterpart to business-facing pillars and the Senior Director, translating recurring business questions into reusable assets and understanding the decisions and business context behind those questions. The position is hybrid and based at Proofpoint’s Sunnyvale, California headquarters, four days per week. Success includes implementing governed ARR, NARR, and retention metrics validated against Finance definitions; shifting priority use cases to shared, tested data assets; establishing an initial governed AI-native analytics capability; incorporating acquired-company data as integration progresses; and measurably reducing manual reconciliation and redundant analytical work.

Requirements

At least 8 years of experience in analytics engineering, data engineering, business intelligence, or data architecture, including 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.
Ability to start from the business question, understand the decision a metric supports and the impact of errors, and clarify why a definition is used before designing its implementation.
Track record leading technical initiatives across teams without formal authority and translating ambiguous business requirements into architecture.
Strong written and verbal communication skills with technical and finance/business stakeholders.

Nice To Haves

Experience with Snowflake, Power BI, and dbt.
Experience with SaaS or recurring-revenue metrics such as ARR, retention, and cohorts.
Hands-on experience with semantic layers, metric stores, or context layers built for LLM or agent consumption, such as Cortex Analyst, dbt Semantic Layer, Cube, or similar.
Experience integrating acquired-company data into enterprise models.
Familiarity with CRM, ERP, and financial planning systems as analytical sources.

Responsibilities

Design and build governed analytical data models and a semantic layer in Snowflake, implementing approved ARR, NARR, retention, and customer-lifecycle definitions as reusable, versioned calculation logic.
Design a machine-readable metric and context registry so that definitions, relationships, and calculations are interpreted consistently across Power BI, self-service tools, and AI-driven interfaces.
Integrate acquired businesses into the governed metric layer, including entity mapping, definition reconciliation, and historical restatement.
Design and implement AI-native analytical capabilities using Snowflake’s Cortex ecosystem and governed semantic models; evaluate architectures for reliable natural-language and agent-based access and recommend approaches that scale on the standardized platform.
Evolve Power BI from fragmented functional reporting toward integrated cross-functional views connecting commercial activity, product performance, and financial outcomes.
Implement reconciliation, validation, lineage, and monitoring controls to make analytical outputs reproducible, traceable, suitable for financial reporting governance, and supportive of audit.
Set technical standards and reusable patterns for modeling, dbt usage, testing, and documentation so analysts and engineering partners can build consistently without central bottlenecks.
Identify upstream data-quality and architectural issues and drive their resolution with system owners.
Partner with IT and Data Engineering to establish analytical requirements, interfaces, validation standards, and data models for consistent downstream use.
Translate recurring business questions into reusable analytical assets and understand the business decisions those questions support.