mozilla/docker-etl
Oct 2025 – Jun 2026
3 Months active
Languages Used
DockerfilePythonShellYAML
Technical Skills
BigQueryCI/CDCloud ServicesData CollectionData EngineeringDocker
Masha Lifshin
PROFILE
Masha Lifshin
Worked on the mozilla/docker-etl repository to deliver robust data engineering solutions focused on experiment analytics and remote settings ingestion. Built an end-to-end ETL pipeline using Python and Docker to collect incrementality metrics, integrating with Google Cloud Storage and BigQuery for configuration and aggregation. Enhanced the Ads Incrementality DAP Collector by refactoring parsing logic and switching to bucket-based indexing, improving data accuracy and reliability. Extended the ingestion pipeline to support schema evolution by gracefully handling unknown fields in remote settings JSON, leveraging API integration and resilient error handling. Emphasized maintainable code, comprehensive testing, and scalable data processing throughout the development cycle.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
2,936
Activity Months3
Your Network
413 peopleSame Organization
@mozilla.com401
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 performance summary for mozilla/docker-etl: Delivered robust remote settings ingestion with KintoSuggestion UUID. Enhanced ingestion to gracefully handle additive changes in remote settings JSON schema by logging and ignoring unknown fields, preventing pipeline breaks. Extended quicksuggest2bq job to include suggestion UUIDs. These changes improve data quality, reduce downtime risk, and strengthen pipeline resilience. Technologies demonstrated include Python ETL, JSON schema handling, UUID integration, and robust logging. Business value: more reliable data ingestion, easier schema evolution, and faster onboarding of new fields.
1 Commits • 1 Features
Jun 1, 2026June 2026 performance summary for mozilla/docker-etl: Delivered robust remote settings ingestion with KintoSuggestion UUID. Enhanced ingestion to gracefully handle additive changes in remote settings JSON schema by logging and ignoring unknown fields, preventing pipeline breaks. Extended quicksuggest2bq job to include suggestion UUIDs. These changes improve data quality, reduce downtime risk, and strengthen pipeline resilience. Technologies demonstrated include Python ETL, JSON schema handling, UUID integration, and robust logging. Business value: more reliable data ingestion, easier schema evolution, and faster onboarding of new fields.
June 2026
November 2025
1 Commits
Nov 1, 2025November 2025: Delivered a critical fix to Ads Incrementality DAP Collector indexing in mozilla/docker-etl to switch from order-based indexing to bucket-based indexing, significantly improving data collection accuracy and reliability of incrementality results. Updated tests to reflect the new indexing logic and ensured maintainability. Commit 852d60ea40169784ed2d8b738b43839848c244f2 (fix(AE-1121)) encapsulates the change.
November 2025
1 Commits
Nov 1, 2025November 2025: Delivered a critical fix to Ads Incrementality DAP Collector indexing in mozilla/docker-etl to switch from order-based indexing to bucket-based indexing, significantly improving data collection accuracy and reliability of incrementality results. Updated tests to reflect the new indexing logic and ensured maintainability. Commit 852d60ea40169784ed2d8b738b43839848c244f2 (fix(AE-1121)) encapsulates the change.
October 2025
2 Commits • 1 Features
Oct 1, 2025Concise monthly summary for 2025-10: Delivered end-to-end Incrementality Metrics Data Collection for mozilla/docker-etl and implemented targeted improvements to the ads_incrementality_dap_collector, delivering business value through reliable experiment data, faster feedback loops, and improved data quality. Key outcomes: End-to-end ETL to fetch experiment configurations from GCS, collect data from Nimbus and DAP, and write aggregated results to BigQuery. Dockerized deployment with tests and robust error handling. Fixed major parsing and token handling issues to support the new dapIncrementality feature and improved parsing for referrer and visit measurements. Impact: More accurate incrementality measurements, enabling faster experiment decision-making and scalable analytics.
2 Commits • 1 Features
Oct 1, 2025Concise monthly summary for 2025-10: Delivered end-to-end Incrementality Metrics Data Collection for mozilla/docker-etl and implemented targeted improvements to the ads_incrementality_dap_collector, delivering business value through reliable experiment data, faster feedback loops, and improved data quality. Key outcomes: End-to-end ETL to fetch experiment configurations from GCS, collect data from Nimbus and DAP, and write aggregated results to BigQuery. Dockerized deployment with tests and robust error handling. Fixed major parsing and token handling issues to support the new dapIncrementality feature and improved parsing for referrer and visit measurements. Impact: More accurate incrementality measurements, enabling faster experiment decision-making and scalable analytics.
October 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability82.6%
Architecture82.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfilePythonShellYAML
Technical Skills
API integrationBigQueryCI/CDCloud ServicesData CollectionData EngineeringDockerETLExperimentationGCPGoogle Cloud StoragePythonbackend developmentdata processingtesting
Repositories Contributed To
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