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Kathryn Thompson

PROFILE

Kathryn Thompson

Kathryn Thompson enhanced data engineering workflows in the Planning-Inspectorate/odw-synapse-workspace repository by building robust HR data processing pipelines and integrating new data sources for analytics readiness. She improved error handling and observability using Python, refining logging decorators and implementing retry logic to increase reliability and traceability across Jupyter Notebooks. Kathryn also delivered DaRT LPA and Horizon-LPA pipeline integrations, stabilizing data flows and supporting downstream analytics. In the Planning-Inspectorate/data-model repository, she standardized API response schemas and documentation for DaRT and GetTimesheets APIs, applying data modeling and schema definition skills to improve contract consistency and accelerate client onboarding.

Overall Statistics

Feature vs Bugs

75%Features

Repository Contributions

8Total
Bugs
1
Commits
8
Features
3
Lines of code
12,270
Activity Months3

Work History

January 2025

1 Commits • 1 Features

Jan 1, 2025

Month: 2025-01 Key deliverables centered on API schema standardization and documentation for critical DaRT and GetTimesheets APIs within the Planning-Inspectorate/data-model repository. This work established consistent, well-documented data contracts, improving reliability for downstream integrations and accelerating onboarding for new API consumers. No major bug fixes were reported this month.

December 2024

1 Commits • 1 Features

Dec 1, 2024

December 2024: Implemented DaRT LPA Data Integration and Horizon-LPA pipelines in odw-synapse-workspace. Added dataset DaRT_LPA_Entity_Source, created raw/sink data flows, updated existing pipelines/notebooks to incorporate these changes, and introduced the rel_6_0_1 pipeline. Focused on stabilizing data flow, improving data quality, and preparing for downstream analytics with Horizon-LPA.

November 2024

6 Commits • 1 Features

Nov 1, 2024

In November 2024, the odw-synapse-workspace project focused on making the HR data processing pipeline more robust and observable. A bug fix hardened the raw-to-standardized HR data conversion by expanding error handling and stabilizing the related notebooks. A new observability feature set added comprehensive logging, refined Python logging decorators, and retry logic across multiple notebooks to improve traceability, debugging, and reliability. Together, these changes reduce downtime, improve data quality, and enable faster incident diagnosis. Technologies demonstrated include Python error handling, decorators, and notebook modernization within Planning-Inspectorate/odw-synapse-workspace.

Activity

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Quality Metrics

Correctness77.6%
Maintainability77.6%
Architecture67.6%
Performance67.6%
AI Usage20.0%

Skills & Technologies

Programming Languages

JSONJupyter NotebookPythonSQLTypeScript

Technical Skills

API DesignData EngineeringData ModelingData ScienceData TransformationData WarehousingETLError HandlingLoggingNotebook DevelopmentPipeline OrchestrationPythonPython DevelopmentSchema Definition

Repositories Contributed To

2 repos

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

Planning-Inspectorate/odw-synapse-workspace

Nov 2024 Dec 2024
2 Months active

Languages Used

Jupyter NotebookPythonSQL

Technical Skills

Data EngineeringData ScienceData TransformationETLError HandlingLogging

Planning-Inspectorate/data-model

Jan 2025 Jan 2025
1 Month active

Languages Used

JSONTypeScript

Technical Skills

API DesignData ModelingSchema Definition

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