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Damon McCullough

PROFILE

Damon Mccullough

During six months on the NYCPlanning/data-engineering repository, Daniel McCullough delivered eight features and two bug fixes focused on data product foundations, spatial analytics, and export pipelines. He built modular ingest, transform, and export workflows for HUD and CBBR datasets, automated CI/CD with GitHub Actions, and enhanced data quality through SQL-based corrections and schema updates. Daniel integrated SFTP using Paramiko, improved S3-compatible storage handling in Docker, and standardized issue tracking with Markdown templates. His work leveraged Python, SQL, and Bash, demonstrating depth in data engineering, DevOps, and geospatial data processing while improving reliability, maintainability, and data governance across the platform.

Overall Statistics

Feature vs Bugs

80%Features

Repository Contributions

12Total
Bugs
2
Commits
12
Features
8
Lines of code
1,578
Activity Months6

Work History

September 2025

1 Commits • 1 Features

Sep 1, 2025

September 2025: Delivered critical CBBR export data quality improvements for NYCPlanning/data-engineering. Implemented schema enhancements by adding policy_area and need_group columns to CBBR export SQL, refactored development bucket handling in the CI/CD pipeline to improve deployment reliability, and corrected mapping of agency response fields to boost data completeness and accuracy of CBBR exports. These changes enhance downstream analytics, reduce data quality issues in production, and support more accurate policy-level reporting.

June 2025

4 Commits • 1 Features

Jun 1, 2025

June 2025 monthly summary for NYCPlanning/data-engineering: Focused on stabilizing storage interactions with S3-compatible services and improving data updates governance. Delivered reliable data update workflows and enhanced issue-tracking visibility through GitHub templates and forms.

May 2025

2 Commits • 2 Features

May 1, 2025

May 2025 performance summary for NYCPlanning/data-engineering: Delivered two feature sets and strengthened data reliability and security. kpdb data extraction/export enhancements improved robustness, Excel compatibility, and added FGDB export with version-based upload routing. Replaced deprecated FTP with a Paramiko-based SFTP solution, including a development environment for SFTP and comprehensive unit/integration tests. These changes reduce downstream errors, improve secure data transfers, and align with data product patterns.

April 2025

2 Commits • 2 Features

Apr 1, 2025

During April 2025, delivered two high-impact features in NYCPlanning/data-engineering that expand data enrichment and export capabilities. These changes enhance spatial analytics coverage for CEQR and automate LION data distribution to downstream systems, enabling faster decision-making and more interoperable datasets.

February 2025

2 Commits • 1 Features

Feb 1, 2025

February 2025 monthly summary for NYCPlanning/data-engineering: Key achievements include a data quality fix for CPDB spatial data and the addition of ArcGIS Online feature service ingestion. The CPDB bug corrected inverted longitude values for point geometries in out-of-region NYC projects and added a build visibility echo. The ArcGIS Online ingestion introduces ESRIFeatureServer as a new source type, updating data models, ingestion configuration, extraction utilities, and tests to enable seamless ingestion of ArcGIS Online datasets. These changes improve data accuracy for planning analytics, expand data source coverage, and strengthen the robustness of ingestion pipelines.

December 2024

1 Commits • 1 Features

Dec 1, 2024

December 2024: Delivered the CDBG data product foundation for NYCPlanning/data-engineering, establishing the data product folder, a dbt project, and ingest/transform/export pipelines for HUD data. Implemented GitHub Actions CI/CD to automate builds, tests, and deployments. Initial build committed: 2bac1f3838a4571d54f73a8d679a44c3fa13b86d ("setup for initial build of new cdbg data product (#1355)"). Major bugs fixed: none reported this month. Business impact: provides a scalable data product foundation enabling timely HUD data analytics and faster onboarding of new datasets. Technologies demonstrated: GitHub Actions, dbt, data ingestion/ETL, and modular data-product architecture.

Activity

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Quality Metrics

Correctness88.4%
Maintainability87.6%
Architecture87.6%
Performance75.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

BashMarkdownPythonSQLShellYAML

Technical Skills

API IntegrationAWS S3CI/CDCloud StorageData EngineeringData IngestionData ManagementData ProcessingDatabase ExportDatabase ManagementDevOpsDockerDocumentationETLGIS Data Management

Repositories Contributed To

1 repo

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

NYCPlanning/data-engineering

Dec 2024 Sep 2025
6 Months active

Languages Used

BashPythonSQLYAMLMarkdownShell

Technical Skills

AWS S3CI/CDData EngineeringDockerETLPostgreSQL

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