Senior Data Engineer (Online Data)

Zoom•Seattle, WA

2d•$98,900 - $228,700•Hybrid

About The Position

The Senior Data Engineer will design, build, and optimize data pipelines and analytical solutions that support Zoom’s Online business, including billing, checkout, growth, and revenue analytics. The role owns the data engineering lifecycle end to end, from ETL design and ingestion through data quality, performance tuning, and delivery of models to stakeholders across Data Science, Finance, and Product. It is a senior individual contributor role within the Data Systems and Online Technology organization. The position is hybrid and requires on-site presence three days per week at either the Seattle, Washington or San Jose, California office. Remote arrangements are not available. The Online Data team provides data infrastructure and insights for Zoom’s Online business and works with Data Engineers, Machine Learning Engineers, and Data Visualization Engineers to provide reliable, well-governed data to decision-makers.

Requirements

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

Responsibilities

Design and implement scalable ETL workflows and data pipelines that transform raw data into analytics-ready formats, with automated validation, monitoring, and reliability checks.
Build analytical datasets from clickstream, user interaction, and product usage events for 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 stakeholders.
Implement automated data quality frameworks for 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, integrate machine learning models into production, and provide technical guidance to peers across data engineering and ML workflows.