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# Software Engineer

- **Company:** Zoom Video Communications, Inc.
- **Location:** San Jose, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $124,000.0 - $271,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Automation of Tests, Cloud Database, Code Review, Continuous Integration, Data Architecture, Data Mart, Data Systems, Data Vault Modeling, Cursor (Graphical User Interface Elements), Dimensional Modeling, Python (Programming Language), Automation of Marketing, Rapid Prototyping Process, Salesforce.Com, Software Engineering, SQL Databases, Data Streaming, Application Enhancement Tool, Test-Driven Development (TDD), Sql Optimization, GitHub Copilot, Large Language Models, Snowflake, Containerization, Information Technology, Apache Kafka, Code Restructuring, Webhooks, Software Version Control, Data Pipelines, Docker, Databricks
- **Published:** August 1, 2026
- **Apply:** https://dejobs.org/x/x/748A0004A2C140B383A1E8B843BC4847/job/

## About the Role

* Have a Bachelor's in Computer Science or Software Engineering. Brings 6+ years of experience in software engineering, data architecture, or forward-deployed engineering and a track record of shipping production-grade data systems.
 * Have expert proficiency in Python and advanced SQL, hands-on experience in cloud data warehouses or lakehouses (Snowflake, Databricks), transformation tooling (dbt), and orchestration (Airflow, Dagster).
 * Demonstrate excellent background in data modeling methodologies (Kimball, Data Vault, dimensional modeling, semantic layer design) applied to complex enterprise domains. Ideally revenue operations or Go-To-Market contexts such as Salesforce, marketing automation, or product telemetry.
 * Demonstrate use of AI coding tools (such as Claude Code, GitHub Copilot, Cursor) and agentic workflows as an integrated part of daily engineering. Not just experimentation along with genuine interest in embedding LLMs into data workflows (text-to-SQL, automated cataloging, generative data quality testing).
 * Work knowledge of real-time event streaming and ingestion architectures (Kafka, event buses, webhooks), familiarity with modern software practices including CI/CD, containerization (Docker), and test-driven development.
 * Have a forward-deployed mindset consultative, resilient in ambiguity, and focused on user impact and delivery speed. With the ability to communicate complex architectural trade-offs clearly to non-technical stakeholders while maintaining credibility with engineering teams.

## Description

Design, build, and own the data pipelines, models, and products that power sales, customer success, and revenue analytics for Zoom's Go-To-Market organization. Partner with business and finance teams to build data systems that power both human analysts and AI agents. Build and ship data products end-to-end using AI-powered tools with full autonomy over the pipeline.

About the Team

We build the data foundations that power Zoom's revenue operations. Our team partners across Go-To-Market, Finance, and Engineering to deliver trusted, scalable data products for both business stakeholders and AI-driven workflows. We value curiosity, collaboration, and a shared commitment to making data accessible and actionable for every team we support.

Responsibilities

 * Acting as the technical lead/primary engineer for Go-To-Market, Revenue Operations, and Sales translating complex business problems, including forecasting, attribution, and churn, into production-grade data products.
 * Treating AI as core engineering multiplier: use agentic tools (like Claude Code, Cursor, and GitHub Copilot) for rapid prototyping, architecture design, refactoring, and code review. Build LLM-assisted workflows such as automated schema mapping and natural-language query layers.
 * Designing and build data models (Kimball, Data Vault), scalable pipelines that ingest and transform Go-To-Market data from Salesforce, product usage, billing, and customer success systems.
 * Building unified semantic layers and data marts that connect diverse source systems. Establish data contracts, schema governance, and lineage standards to ensure reliable downstream consumption.
 * Balancing real-time streaming architectures (Kafka, event-driven pipelines) with modern batch warehouse patterns, working across Snowflake/Databricks, DBT, Fivetran , and Airflow.
 * Applying software engineering discipline CI/CD, automated testing, version control to build data quality, observability, and monitoring frameworks; mentor peers, contribute to documentation and governance reviews.


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