mozilla/bigquery-etl
Mar 2026 – Jun 2026
3 Months active
Languages Used
PythonYAMLSQL
Technical Skills
BigQueryETL processesPrometheuscloud computingcloud engineeringdata engineering
wil stuckey
PROFILE
Wil Stuckey
Contributed to the mozilla/bigquery-etl repository by building and enhancing data engineering workflows and access controls for MozCloud and Bugzilla metrics. Developed automated ETL pipelines using Python and SQL to improve data freshness, reliability, and uptime visibility, including a daily BigQuery task runner and a dynamic uptime monitoring dataset with Prometheus integration. Enhanced data accessibility and governance by introducing public views, refining metadata, and implementing granular access control for workgroups, leveraging YAML for configuration management. Focused on reducing operational toil, supporting faster incident response, and strengthening permissions models, these efforts improved data quality, security, and management across cloud infrastructure and analytics teams.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
5
Lines of code
545
Activity Months3
Your Network
454 peopleSame Organization
@mozilla.com401
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026: Delivered Access Control Enhancement for Bugzilla Metrics Data Viewers in mozilla/bigquery-etl. Implemented backstage workgroup addition to dataViewers within Bugzilla metrics, enabling granular, auditable access controls for data viewing. Commit 3921d52d26aee34f709aca90f869310886304702: feat(bugzilla_metrics.users): Add backstage to dataViewers (#9635). Result: improved security posture and clearer data-sharing boundaries for analytics teams.
1 Commits • 1 Features
Jun 1, 2026June 2026: Delivered Access Control Enhancement for Bugzilla Metrics Data Viewers in mozilla/bigquery-etl. Implemented backstage workgroup addition to dataViewers within Bugzilla metrics, enabling granular, auditable access controls for data viewing. Commit 3921d52d26aee34f709aca90f869310886304702: feat(bugzilla_metrics.users): Add backstage to dataViewers (#9635). Result: improved security posture and clearer data-sharing boundaries for analytics teams.
June 2026
May 2026
4 Commits • 2 Features
May 1, 2026May 2026 monthly summary for mozilla/bigquery-etl: Delivered MozCloud workgroup data access and visibility enhancements and Backstage GKE onboarding with access control. These changes strengthen data accessibility, ownership context, and permissions governance for MozCloud datasets, laying groundwork for safer data sharing and easier data discovery across teams.
May 2026
4 Commits • 2 Features
May 1, 2026May 2026 monthly summary for mozilla/bigquery-etl: Delivered MozCloud workgroup data access and visibility enhancements and Backstage GKE onboarding with access control. These changes strengthen data accessibility, ownership context, and permissions governance for MozCloud datasets, laying groundwork for safer data sharing and easier data discovery across teams.
March 2026
2 Commits • 2 Features
Mar 1, 2026March 2026 (2026-03) performance highlights for the mozilla/bigquery-etl project. Deliveries include the Mozcloud Uptime Monitoring Dataset and the Mozcloud Daily BigQuery ETL Task Runner. The Mozcloud Uptime Monitoring Dataset introduces a new dataset to track Mozcloud service uptime metrics with dynamically calculated thresholds, refined data workflow through improved query logic for data consistency, refined metadata, and retry logic for Prometheus queries. The Mozcloud Daily BigQuery ETL Task Runner adds a new daily DAG to automate Cloud Engineering BigQuery ETL tasks for Mozcloud datasets, enhancing data freshness and processing reliability. Overall impact: higher uptime visibility, more consistent and reliable metrics, fewer query failures due to retry logic, and reduced manual toil through automation. This work supports faster incident response and improved SLA reporting, driving trust with stakeholders. Technologies/skills demonstrated include BigQuery, Airflow DAGs, Python-based ETL, query optimization (including or vector() and constant-rate window adaptations), metadata management, and Prometheus integration.
2 Commits • 2 Features
Mar 1, 2026March 2026 (2026-03) performance highlights for the mozilla/bigquery-etl project. Deliveries include the Mozcloud Uptime Monitoring Dataset and the Mozcloud Daily BigQuery ETL Task Runner. The Mozcloud Uptime Monitoring Dataset introduces a new dataset to track Mozcloud service uptime metrics with dynamically calculated thresholds, refined data workflow through improved query logic for data consistency, refined metadata, and retry logic for Prometheus queries. The Mozcloud Daily BigQuery ETL Task Runner adds a new daily DAG to automate Cloud Engineering BigQuery ETL tasks for Mozcloud datasets, enhancing data freshness and processing reliability. Overall impact: higher uptime visibility, more consistent and reliable metrics, fewer query failures due to retry logic, and reduced manual toil through automation. This work supports faster incident response and improved SLA reporting, driving trust with stakeholders. Technologies/skills demonstrated include BigQuery, Airflow DAGs, Python-based ETL, query optimization (including or vector() and constant-rate window adaptations), metadata management, and Prometheus integration.
March 2026
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Quality Metrics
Correctness97.2%
Maintainability94.2%
Architecture94.2%
Performance94.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQLYAML
Technical Skills
BigQueryETL processesPrometheusSQLaccess controlcloud computingcloud engineeringcloud infrastructurecloud servicesdata access controldata engineeringdata managementdatabase management
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