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JR Conlin

PROFILE

Jr Conlin

Over a two-month period, contributed to backend data engineering for Mozilla’s merino-py and telemetry-airflow repositories, focusing on sports data integration and workflow orchestration. Developed and integrated a SportsData provider in merino-py, implementing ingestion jobs, locale-aware team display names, and expanded unit test coverage using Python and Elasticsearch. In telemetry-airflow, delivered DAG-based sports data processing with secure secrets management, frequent scheduling, and notification controls, leveraging Apache Airflow and Kubernetes. Enhanced documentation and code maintainability while improving data pipeline reliability and operational security. The work enabled timely, reliable sports data ingestion and processing, supporting both feature delivery and maintainable infrastructure.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

10Total
Bugs
0
Commits
10
Features
2
Lines of code
4,259
Activity Months2

Work History

November 2025

9 Commits • 1 Features

Nov 1, 2025

November 2025: Delivered the Sports Data DAGs and Secrets Management feature in mozilla/telemetry-airflow, enabling end-to-end sports data processing with updated scheduling, data purging, notification controls, ES configuration, and secure handling of API keys and secrets. Implemented cadence and reliability improvements (5-minute updates; hourly pause during debugging) and tightened secret management (explicit ES URL, included secrets, and API key env var corrections). Temporary alert suppression (email on failure) reduced alert noise during rollout. Documentation improvements added inline comments for merino_jobs to aid maintainability. Business value: timelier data, improved security, and reduced operational risk.

October 2025

1 Commits • 1 Features

Oct 1, 2025

October 2025: Delivered the SportsData provider integration for merino-py, including ingestion jobs and support for mixed_sports recommendations. Implemented locale support for team display names, enhanced the query builder, and expanded metrics to track provider usage. Strengthened unit test coverage and ensured end-to-end reliability of the provider path.

Activity

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Quality Metrics

Correctness94.0%
Maintainability88.0%
Architecture88.0%
Performance88.0%
AI Usage24.0%

Skills & Technologies

Programming Languages

Python

Technical Skills

API developmentAPI integrationAirflowApache AirflowBackend DevelopmentData EngineeringElasticsearchKubernetesPythonPython scriptingbackend developmentdata engineeringdata ingestiondata pipeline developmentdocumentation

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

mozilla/telemetry-airflow

Nov 2025 Nov 2025
1 Month active

Languages Used

Python

Technical Skills

API integrationAirflowApache AirflowBackend DevelopmentData EngineeringKubernetes

mozilla-services/merino-py

Oct 2025 Oct 2025
1 Month active

Languages Used

Python

Technical Skills

API developmentElasticsearchbackend developmentdata ingestionunit testing