Month: 2025-01 — Focused on strengthening native desktop ad analytics in mozilla/metric-hub. Delivered a key feature to track native desktop ad performance by adding spend, dismisses, clicks, saves, and their rates in ads.toml, sourced from the native_desktop_ad_metrics_hourly data source. This supports more accurate attribution and optimization of native desktop ads. Commit: 098b00e4a22dbd31cfc3d28af3ba3d582f20f234 (Add NewTab Ad Metrics to ads.toml (#717)).

November 2024 monthly summary: Achievements across bigquery-etl and Looker components focused on tentpole dates coverage and analysis integration. Key outputs include expanded 2020–2023 Key Tentpole Dates dataset with major holidays (Thanksgiving, Black Friday, Cyber Monday, Giving Tuesday, Cyber Five) and corrected naming to 'Cyber Five', plus 2023 entries and spelling fix. Delivered Tentpole Dates Looker integration by creating the key_tentpole_dates view and wiring it into newtab_clients_daily, newtab_interactions, newtab_visits, and active_users_aggregates, with updates to explores and file paths. Also performed deprecation/cleanup by removing tentpole-based joins from several explores to simplify the data model. Result: improved campaign attribution and forecasting accuracy, better downstream reporting, and reduced maintenance overhead. Technologies/skills demonstrated: LookerML (views, explores, joins), Looker file path conventions, SQL/ETL data updates, data modeling and governance.

Month 2024-10 — Delivered a new Advertising Tentpole Dates Dataset Integration within the mozilla/bigquery-etl project. This feature adds a dedicated dataset to track key tentpole events, including a CSV with event names, start/end dates, metadata, and a defined schema, and it integrates into the existing data model to enable analysis and planning. No major bugs reported this month. The work improves analytics readiness for advertising campaigns, enhances data governance, and provides a reusable data asset for business insights. Technologies and practices demonstrated include data modeling, CSV/schema management, ETL/data-pipeline readiness, metadata handling, and Git-based version control for traceability.