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# Data Engineer (Online Data)

- **Company:** Zooms, Inc.
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $98,900 - $228,700
- **Contract:** Permanent contract
- **Skills:** Airflow, Databases, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Systems, Database Queries, E-Business, Human-Computer Interaction, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Meta-Data Management, Performance Tuning, Query Optimization, SQL Databases, Unstructured Data, Web Analytics, Snowflake, Database Optimization, Apache Spark, Caching, Information Technology, Machine Learning Operations, Data Pipelines, User Identification, Databricks
- **Published:** November 6, 2026
- **Apply:** https://startup.jobs/senior-data-engineer-online-data-zoom-10696083

## About the Role

* Data engineering expertise: 8+ years of hands-on experience designing and optimizing complex data pipelines and ETL workflows, including error handling, monitoring, and performance tuning, using tools such as Snowflake, Databricks, Spark, Airflow, dbt, and Python.
 * SQL and database mastery: Proficiency designing normalized schemas, optimizing complex queries, and applying advanced database features for high-performance analytical workloads.
 * Machine learning and statistical methods: Experience developing and optimizing ML solutions, designing and executing hypothesis testing strategies, and applying statistical techniques to validate analytical approaches.
 * Web analytics and product telemetry: Familiarity with event tracking, clickstream data, sessionization, and user identity resolution, with demonstrated experience building analytical datasets from web and product usage data.
 * Data governance and quality: Experience implementing data standards, metadata management, lineage, ownership, and access controls, combined with the ability to design automated validation tests and anomaly detection to maintain data reliability.
 * Communication and visualization: Ability to build complex, interactive visualizations and communicate findings clearly to both technical and non-technical stakeholders, with strong skills in independently managing stakeholder relationships.
 * Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience.

## Description

In this role, you will design, build, and optimize the data pipelines and analytical solutions that power Zoom's Online business, including billing, checkout, growth, and revenue analytics. Working end to end, you will own the full data engineering lifecycle, from ETL design and ingestion through data quality, performance tuning, and model delivery to stakeholders across Data Science, Finance, and Product. Each day brings a mix of deep technical work and meaningful cross-functional collaboration, with opportunities to shape how the Online business understands and acts on its data. This is a senior individual contributor role reporting into the Data Systems and Online Technology organization. This is a hybrid position that requires on-site presence three (3) days per week at our Seattle, WA or San Jose, CA office. Remote arrangements are not available for this role., * Design and implement scalable ETL workflows and data pipelines that transform raw data into structured, analytics-ready formats, with automated validation checks and monitoring to ensure ongoing quality and reliability.

 * Build analytical datasets from clickstream, user interaction, and product usage events to support web analytics, feature adoption analysis, and user journey insights.
 * Establish and maintain data governance practices, including data definitions, lineage, ownership documentation, and access controls for sensitive data, in collaboration with cross-functional stakeholders.
 * Implement automated data quality frameworks covering accuracy, completeness, consistency, and freshness, and establish alerting to identify and resolve data issues proactively.
 * Optimize pipeline performance and database efficiency through query tuning, indexing strategies, caching configurations, and model accuracy evaluation for large-scale structured and unstructured data.
 * Analyze complex datasets using statistical techniques and machine learning methods, build interactive visualizations, and run hypothesis testing to surface insights for technical and non-technical audiences.
 * Lead end-to-end development of data products from concept through deployment, integrating machine learning models into production and providing technical guidance to peers across data engineering and ML workflows.