Senior Data Engineer

Zoom•San Jose, CA

1d•Hybrid

About The Position

Immigration sponsorship is not available for this position. You will design and scale the data systems that connect product usage to revenue outcomes. You will build real-time and batch pipelines powering predictive models and business activation. You will shape foundational architecture that drives company-wide decision-making. Our team builds the data platform powering revenue intelligence across products. We partner with engineering, data science, and go-to-market teams. We turn raw telemetry into trusted, actionable data at scale.

Requirements

Demonstrate extensive experience designing and operating large-scale data platforms in production environments with measurable business impact.
Build production-grade data pipelines using Python and advanced SQL, with a focus on performance optimization and reliability.
Operate modern data warehouses (such as Snowflake or Databricks) and orchestration tools (such as Airflow or Dagster) to manage complex data workflows.
Integrate diverse data sources — including event telemetry, CRM, and billing systems — into cohesive, well-modeled data products.
Apply data modeling best practices (using dbt or equivalent) to create testable, documented, and backward-compatible models that enable self-service analytics.
Bring experience in SaaS or product-led growth environments, including familiarity with product analytics platforms or streaming systems.
Support machine learning workflows by building feature stores, training pipelines, or real-time scoring infrastructure, or demonstrate equivalent practical experience.
Leverage AI-assisted development tools to accelerate engineering workflows, contribute to reusable automation, and continuously adopt emerging tooling to enhance team productivity.

Responsibilities

Designing and building end-to-end data architecture that unifies product telemetry, customer lifecycle, and revenue metrics into a trusted single source of truth.
Developing and maintaining scalable pipelines — both real-time and batch — that integrate product usage, CRM, and billing systems to enable closed-loop revenue attribution.
Leading implementation of a standardized telemetry framework across platforms, defining event contracts and solving identity resolution challenges across devices and products.
Building data foundations for machine learning by partnering with data science to deliver feature pipelines, training datasets, and low-latency scoring infrastructure that accelerate experimentation.
Establishing reliability and governance standards including data quality frameworks, monitoring, incident response, privacy compliance, and self-service data modeling practices.