mozilla/bigquery-etl
Mar 2026 – Mar 2026
1 Month active
Languages Used
PythonYAML
Technical Skills
BigQueryETL processesPrometheuscloud computingcloud engineeringdata engineering
wil stuckey
PROFILE
Wil Stuckey
Wes Stuckey developed two core features for the mozilla/bigquery-etl repository, focusing on Mozcloud’s data reliability and automation. He built the Mozcloud Uptime Monitoring Dataset, which tracks service uptime using dynamically calculated thresholds and refined query logic to ensure consistent metrics and improved metadata. Stuckey also implemented the Mozcloud Daily BigQuery ETL Task Runner, automating daily ETL workflows to enhance data freshness and reduce manual intervention. His work leveraged Python, BigQuery, and Prometheus, incorporating retry logic and query optimizations such as vector operations and constant-rate windowing. These contributions improved data quality, operational efficiency, and incident response capabilities for cloud engineering.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
497
Activity Months1
Your Network
407 peopleSame Organization
@mozilla.com364
Work History
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
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
BigQueryETL processesPrometheuscloud computingcloud engineeringdata engineering
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