Prof-Drake-UMD/INST767-Sp25
Mar 2025 – May 2025
3 Months active
Languages Used
JSONMarkdownPythonSQLDockerfileHTML
Technical Skills
API IntegrationAirflowBigQueryCloud ComputingData EngineeringDocumentation
Chien-Chi Liu
PROFILE
Chien-chi Liu
Jessica Tinex developed a scalable, cloud-native data integration and analytics platform for the Prof-Drake-UMD/INST767-Sp25 repository, focusing on job market data. She designed and implemented a unified ETL pipeline in Python to ingest and standardize postings from multiple APIs, including Adzuna, Jooble, and Muse, producing a single JSON output and updating the data schema. Leveraging Google Cloud Platform technologies such as Pub/Sub, Cloud Functions, and BigQuery, Jessica modernized the ingestion and transformation process for event-driven scalability. Her work established a comprehensive data model, improved documentation, and enhanced data analysis capabilities, demonstrating depth in data engineering and maintainability.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
5
Lines of code
391,173
Activity Months3
Your Network
19 people
Work History
May 2025
5 Commits • 3 Features
May 1, 2025In May 2025, delivered a cloud-native data integration and analytics foundation for job market data in the Prof-Drake-UMD/INST767-Sp25 project. Implemented a unified ETL to ingest and standardize postings from Adzuna, Jooble, and Muse into a single JSON, grouped API connection files under a new directory, and updated the data schema with a new category field. Established a comprehensive data model foundation for job data, including definitions for various job descriptions and details, and produced an end-to-end prototype with CSV export, supported by BigTable and SQL query capabilities. Modernized ingestion/transformation pipeline with Pub/Sub and Cloud Functions to ingest data into Cloud Storage and trigger transformations, enabling scalable, event-driven processing. Fixed BigQuery SQL queries and table definitions, updated documentation, and improved data analysis capabilities to support reliable analytics. Overall, these efforts create a scalable, maintainable data platform, reduce data variance, accelerate analytics, and enable data-driven decisions across the business.
5 Commits • 3 Features
May 1, 2025In May 2025, delivered a cloud-native data integration and analytics foundation for job market data in the Prof-Drake-UMD/INST767-Sp25 project. Implemented a unified ETL to ingest and standardize postings from Adzuna, Jooble, and Muse into a single JSON, grouped API connection files under a new directory, and updated the data schema with a new category field. Established a comprehensive data model foundation for job data, including definitions for various job descriptions and details, and produced an end-to-end prototype with CSV export, supported by BigTable and SQL query capabilities. Modernized ingestion/transformation pipeline with Pub/Sub and Cloud Functions to ingest data into Cloud Storage and trigger transformations, enabling scalable, event-driven processing. Fixed BigQuery SQL queries and table definitions, updated documentation, and improved data analysis capabilities to support reliable analytics. Overall, these efforts create a scalable, maintainable data platform, reduce data variance, accelerate analytics, and enable data-driven decisions across the business.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 Key features delivered: - Job Market Data Pipeline Documentation and API Integrations: Delivered a comprehensive README documenting the pipeline architecture, data sources, components, unified schema, update schedule, API layer, and technologies. Introduced new Python modules for integrating with Adzuna, Jooble, and Muse APIs, including API requests, data extraction, and error handling, with example usage. - Architecture/tech adjustments: README updated to reflect Jooble data source and related architectural/tech adjustments. Major bugs fixed: - No significant bugs fixed this month. Overall impact and accomplishments: - Strengthened maintainability and onboarding for the Job Market Data Pipeline, enabling faster integration of additional data sources (e.g., Jooble) and clearer governance of the data flow. This work lays the foundation for more timely and comprehensive job-market data delivery to stakeholders. Technologies/skills demonstrated: - Python development for API integrations; API design and error handling; robust data extraction; documentation best practices; cross-source data integration; Git version control.
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 Key features delivered: - Job Market Data Pipeline Documentation and API Integrations: Delivered a comprehensive README documenting the pipeline architecture, data sources, components, unified schema, update schedule, API layer, and technologies. Introduced new Python modules for integrating with Adzuna, Jooble, and Muse APIs, including API requests, data extraction, and error handling, with example usage. - Architecture/tech adjustments: README updated to reflect Jooble data source and related architectural/tech adjustments. Major bugs fixed: - No significant bugs fixed this month. Overall impact and accomplishments: - Strengthened maintainability and onboarding for the Job Market Data Pipeline, enabling faster integration of additional data sources (e.g., Jooble) and clearer governance of the data flow. This work lays the foundation for more timely and comprehensive job-market data delivery to stakeholders. Technologies/skills demonstrated: - Python development for API integrations; API design and error handling; robust data extraction; documentation best practices; cross-source data integration; Git version control.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Prof-Drake-UMD/INST767-Sp25: Established project scaffolding to enable onboarding and future feature development; laid groundwork for organized collaboration and maintainability.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Prof-Drake-UMD/INST767-Sp25: Established project scaffolding to enable onboarding and future feature development; laid groundwork for organized collaboration and maintainability.
March 2025
Activity
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Quality Metrics
Correctness87.6%
Maintainability87.6%
Architecture90.0%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfileHTMLJSONMarkdownPythonSQL
Technical Skills
API IntegrationAirflowBigQueryCloud ComputingCloud StorageData EngineeringData ModelingData StandardizationData TransformationDockerDocumentationETLFlaskGoogle Cloud PlatformJupyter Notebook
Repositories Contributed To
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