
Over a three-month period, Temisan contributed to the mozilla/telemetry-airflow repository by building and refining data ingestion workflows using Python, Airflow, and DevOps practices. Temisan engineered per-language task groups for the Merino Wikipedia Indexing Job, enabling scalable, isolated processing for each supported language and improving deployment safety. They also developed and integrated automated ingestion jobs for polygon images and flight schedules, introducing secure API key management and scheduling to ensure reliable, near real-time data availability for analytics. Temisan’s work focused on maintainable, parallelized ETL pipelines, enhancing data completeness and operational visibility without introducing bugs, demonstrating strong backend development depth.

October 2025 monthly summary for mozilla/telemetry-airflow: Delivered a new Flight Schedule Data Ingestion workflow using Airflow to fetch flight schedules from FlightAware, including a new API key secret and a DAG scheduled every six hours to fetch and store flight data for analytics and operational visibility. This work enables near real-time visibility into flight schedules and improves data freshness for downstream analytics and monitoring.
October 2025 monthly summary for mozilla/telemetry-airflow: Delivered a new Flight Schedule Data Ingestion workflow using Airflow to fetch flight schedules from FlightAware, including a new API key secret and a DAG scheduled every six hours to fetch and store flight data for analytics and operational visibility. This work enables near real-time visibility into flight schedules and improves data freshness for downstream analytics and monitoring.
August 2025 — Delivered Polygon Image Ingestion Airflow Job for the telemetry-airflow repository and integrated it into the existing DAG to enable automated polygon image ingestion within the telemetry pipeline. Implemented a dedicated secret for API key access, configured the job to run with specific arguments, and ensured seamless orchestration with other ingestion tasks. The change is tied to DISCO-3633 ([d25354548dfdba5dc8d4b57f60917da7a22bc7d6]), with no major bugs reported this month. Overall impact: improved data completeness for polygon imagery, reduced manual ingestion effort, and more reliable end-to-end ingestion workflows.
August 2025 — Delivered Polygon Image Ingestion Airflow Job for the telemetry-airflow repository and integrated it into the existing DAG to enable automated polygon image ingestion within the telemetry pipeline. Implemented a dedicated secret for API key access, configured the job to run with specific arguments, and ensured seamless orchestration with other ingestion tasks. The change is tied to DISCO-3633 ([d25354548dfdba5dc8d4b57f60917da7a22bc7d6]), with no major bugs reported this month. Overall impact: improved data completeness for polygon imagery, reduced manual ingestion effort, and more reliable end-to-end ingestion workflows.
Month: 2025-05 – Telemetry Airflow: Delivered scalable per-language processing for Merino Wikipedia Indexing. Refactored the Merino Wikipedia Indexing Job to support per-language task groups, enabling language-specific copying and indexing in staging and production. This enables isolated, language-specific workflows, improves parallelism, and reduces cross-language coupling for deploys.
Month: 2025-05 – Telemetry Airflow: Delivered scalable per-language processing for Merino Wikipedia Indexing. Refactored the Merino Wikipedia Indexing Job to support per-language task groups, enabling language-specific copying and indexing in staging and production. This enables isolated, language-specific workflows, improves parallelism, and reduces cross-language coupling for deploys.
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