NYCPlanning/data-engineering
Nov 2024 – Oct 2025
11 Months active
Languages Used
CSSHTMLJinjaPythonSQLYAMLBashMarkdown
Technical Skills
Asset GenerationBuild AutomationCSSData EngineeringData ExportDependency Management
Damon McCullough
PROFILE
Damon Mccullough
Damon McCormick engineered robust data pipelines and analytics infrastructure for the NYCPlanning/data-engineering repository, focusing on scalable data integration, quality, and automation. He modernized ETL workflows, expanded dataset coverage, and improved data governance by implementing versioned ingests, schema alignment, and automated reporting. Leveraging Python, SQL, and dbt, Damon enhanced CI/CD processes, introduced fiscal-year versioning, and streamlined build and deployment pipelines. His work included integrating geospatial datasets, automating Excel and PDF exports, and strengthening data validation and documentation. Through iterative refactoring and test-driven development, Damon delivered maintainable, reliable systems that improved data accessibility, traceability, and downstream analytics for urban planning.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
197Total
Bugs
22
Commits
197
Features
66
Lines of code
39,234
Activity Months11
Your Network
6 people
Work History
October 2025
14 Commits • 5 Features
Oct 1, 2025October 2025 performance summary for the NYCPlanning/data-engineering repo focused on delivering key features, stabilizing nightly builds, refreshing critical datasets, and expanding experimentation and CI/CD capabilities. The work enhances data freshness, infra visibility, data quality, and developer productivity, driving faster, more reliable planning analytics.
14 Commits • 5 Features
Oct 1, 2025October 2025 performance summary for the NYCPlanning/data-engineering repo focused on delivering key features, stabilizing nightly builds, refreshing critical datasets, and expanding experimentation and CI/CD capabilities. The work enhances data freshness, infra visibility, data quality, and developer productivity, driving faster, more reliable planning analytics.
October 2025
September 2025
8 Commits • 2 Features
Sep 1, 2025September 2025: Focused on stabilizing data pipelines and improving data discoverability for NYC Planning datasets. Implemented URL modernization for datasets, added direct source links (Blue Book Enrollment), restored original zoning mappings by reverting high-density adjustments, fixed development DB byte links, migrated and hardened the Doe School Subdistricts ingestion format, and corrected CI workflow run naming for ingestions. These efforts delivered more reliable data access, clearer provenance, and robust ingestion processes.
September 2025
8 Commits • 2 Features
Sep 1, 2025September 2025: Focused on stabilizing data pipelines and improving data discoverability for NYC Planning datasets. Implemented URL modernization for datasets, added direct source links (Blue Book Enrollment), restored original zoning mappings by reverting high-density adjustments, fixed development DB byte links, migrated and hardened the Doe School Subdistricts ingestion format, and corrected CI workflow run naming for ingestions. These efforts delivered more reliable data access, clearer provenance, and robust ingestion processes.
August 2025
11 Commits • 2 Features
Aug 1, 2025Monthly summary for 2025-08 focused on NYCPlanning/data-engineering. Key work includes delivering a new fiscal-year versioning capability in the dcpy library, strengthening data integrity and processing pipelines, and enhancing CI/CD workflows and test visibility. These efforts produced higher data reliability for downstream analytics, improved build/test traceability, and faster, more auditable deployments.
11 Commits • 2 Features
Aug 1, 2025Monthly summary for 2025-08 focused on NYCPlanning/data-engineering. Key work includes delivering a new fiscal-year versioning capability in the dcpy library, strengthening data integrity and processing pipelines, and enhancing CI/CD workflows and test visibility. These efforts produced higher data reliability for downstream analytics, improved build/test traceability, and faster, more auditable deployments.
August 2025
July 2025
23 Commits • 9 Features
Jul 1, 2025Month: 2025-07 | NYCPlanning/data-engineering. Summary of business value and technical achievements: - Features delivered: A2 export flags for job types and descriptions (commit 9dfcd7371501028b5d93424947f1e3d973a4b445); A2 corrections as blacklist/whitelist using latest corrections (commit 8f6678759f14e40d5335b3a21faf310e3c08d075); DBT workflow enhancements including project scaffolding, build commands, and dbt-postgres dependency (commits afb5d43c00c7833d810a75db3483f864f4aaad8c; ad0dcbe0b700a1ace4e31f794839147cb8fce843; 891f6016ed95162b2f83968935fc13f2c9e123d1); Seed models migration from CSV-based tables with source models (commits 888b93a988c87631ce6e6ea49695fd21f4e03355; d19412046d79b5954ff983bdf8481e3acc032336). - Bugs fixed: Typo corrections across repository; removal of unused data and unused variables; label and column name fixes; pilot project tests tightened (commits 35cb05d088a052b3825ece3a81a76fd6e153c7b9; cfe98457d5fdda056a08dd5d06de9b8faf521b3d; 46b75b62d7885f070123cfa4448431b1f483c823; e74325f06c02d149daade6467ef3277e41070273; 990cc1a4bb936dbcb07f226fbccccc7a581af532). - Overall impact and accomplishments: Strengthened data quality, governance, and reproducibility; enabled scalable data product pipelines and safer QA cycles; aligned datasets and tests with module structure. - Technologies/skills demonstrated: dbt, Geosupport, DevDB versioning, seed modeling, data validation, and bash scripting.
July 2025
23 Commits • 9 Features
Jul 1, 2025Month: 2025-07 | NYCPlanning/data-engineering. Summary of business value and technical achievements: - Features delivered: A2 export flags for job types and descriptions (commit 9dfcd7371501028b5d93424947f1e3d973a4b445); A2 corrections as blacklist/whitelist using latest corrections (commit 8f6678759f14e40d5335b3a21faf310e3c08d075); DBT workflow enhancements including project scaffolding, build commands, and dbt-postgres dependency (commits afb5d43c00c7833d810a75db3483f864f4aaad8c; ad0dcbe0b700a1ace4e31f794839147cb8fce843; 891f6016ed95162b2f83968935fc13f2c9e123d1); Seed models migration from CSV-based tables with source models (commits 888b93a988c87631ce6e6ea49695fd21f4e03355; d19412046d79b5954ff983bdf8481e3acc032336). - Bugs fixed: Typo corrections across repository; removal of unused data and unused variables; label and column name fixes; pilot project tests tightened (commits 35cb05d088a052b3825ece3a81a76fd6e153c7b9; cfe98457d5fdda056a08dd5d06de9b8faf521b3d; 46b75b62d7885f070123cfa4448431b1f483c823; e74325f06c02d149daade6467ef3277e41070273; 990cc1a4bb936dbcb07f226fbccccc7a581af532). - Overall impact and accomplishments: Strengthened data quality, governance, and reproducibility; enabled scalable data product pipelines and safer QA cycles; aligned datasets and tests with module structure. - Technologies/skills demonstrated: dbt, Geosupport, DevDB versioning, seed modeling, data validation, and bash scripting.
June 2025
29 Commits • 6 Features
Jun 1, 2025June 2025 — NYCPlanning/data-engineering: Modernized DevDB build, expanded DOB NOW data integration, improved data quality, and cleaned tooling to boost reliability, speed of data categorization, and maintainability. Key features delivered: - DevDB Build and Script Modernization: Removed MinIO setup; migrated DevDB build to workflow steps; refactored scripts for readability; preserved intermittent tables; wired datasource to intermediate DevDB tables. - DOB NOW Data Integration into DevDB and A2/BIS classification: Added work type support, lookup tables, boolean column to speed A2 queries, decoding of DOB NOW work types, BIS inclusion logic. - NOW/DOB Data Enrichment and Robustness: Source column lookup; guard against missing work type and permit type descriptions. - A2 Job Export Enhancements: Added geographic fields; support include/correct of A2 jobs. - DevDB Maintenance and Cleanup: Restore bash utils; artifact cleanup; archive unnecessary files; refactor scripts. Major bugs fixed: - Geometry Bug Fix: Pin dcp_zoningmapamendments to a stable version to avoid geometry-related failures. - Template and Error Message Improvements: Remove bug label from scheduled action failure template; fix f-string for error messages. Overall impact and accomplishments: - Delivered reliable, scalable data pipelines and clearer, maintainable DevDB tooling; improved data categorization accuracy for A2/BIS; reduced technical debt through cleanup and refactoring. Technologies/skills demonstrated: - Data modeling and ETL design (lookup tables, decode logic, boolean optimization) - SQL quality and performance improvements - CI/CD/workflow automation and GitHub Actions - Code readability and refactoring - Data quality controls and error handling
29 Commits • 6 Features
Jun 1, 2025June 2025 — NYCPlanning/data-engineering: Modernized DevDB build, expanded DOB NOW data integration, improved data quality, and cleaned tooling to boost reliability, speed of data categorization, and maintainability. Key features delivered: - DevDB Build and Script Modernization: Removed MinIO setup; migrated DevDB build to workflow steps; refactored scripts for readability; preserved intermittent tables; wired datasource to intermediate DevDB tables. - DOB NOW Data Integration into DevDB and A2/BIS classification: Added work type support, lookup tables, boolean column to speed A2 queries, decoding of DOB NOW work types, BIS inclusion logic. - NOW/DOB Data Enrichment and Robustness: Source column lookup; guard against missing work type and permit type descriptions. - A2 Job Export Enhancements: Added geographic fields; support include/correct of A2 jobs. - DevDB Maintenance and Cleanup: Restore bash utils; artifact cleanup; archive unnecessary files; refactor scripts. Major bugs fixed: - Geometry Bug Fix: Pin dcp_zoningmapamendments to a stable version to avoid geometry-related failures. - Template and Error Message Improvements: Remove bug label from scheduled action failure template; fix f-string for error messages. Overall impact and accomplishments: - Delivered reliable, scalable data pipelines and clearer, maintainable DevDB tooling; improved data categorization accuracy for A2/BIS; reduced technical debt through cleanup and refactoring. Technologies/skills demonstrated: - Data modeling and ETL design (lookup tables, decode logic, boolean optimization) - SQL quality and performance improvements - CI/CD/workflow automation and GitHub Actions - Code readability and refactoring - Data quality controls and error handling
June 2025
April 2025
28 Commits • 16 Features
Apr 1, 2025April 2025 monthly summary for NYCPlanning/data-engineering. The month delivered substantial enhancements to data templates, ingestion and modeling pipelines, and expanded dataset coverage, while tightening quality controls and documentation. The work is aligned with delivering reliable, scalable data products for planning analytics and downstream BI. Key features delivered: - MapPluto Template Updates and Clipped Workflow: refreshed dcp_mappluto_wi and dcp_mappluto_clipped templates and enabled clipped MapPluto workflow (commits e4326f519c7c064038834959bde03d03cc4d9063; d78b263c0bb3a8e9b305d4e6750db6bc7ed3d590; a9def3e42ea334ef29c2e01f729489475124723e). These changes improve map accuracy and performance for planning outputs. - Ingest and Modeling Enhancements: added versioning to ingest action run names, materialized all models as tables, and adopted the latest population projections file from the Housing team (commits 22e39d6ac2ef63587ba31744b42101a02eb9a520; 4e80dccc75a507f8f6889871c20fbc56de048258; 59eb62f5b4fdb57af0065d4b4e51d693d98c2093). - Ceqr Data Templates, Recipe, and Documentation: introduced Ceqr source data templates, updated the Ceqr recipe and readme, and adjusted dataset names for clarity (commits b7acc32dd3ced24da6ea8101cb5ad9a7d9d2dc05; 96497e78308548da516f8d197204e7c51b3c6236; c0dad3185894daacfdf6c699e0f85d8a88dedb43; c0d677276241b8baf8187ae6f8d8e72bd4ac55db). - Add New Datasets (DPR ParksProperties, DCP Lion, etc.): expanded data catalog with DPR ParksProperties, DCP Lion and additional datasets (commits 3e364fe7deb5f7d4ea4a6f1ed304d549336cd22d; 0b16064ec47da89d3abeb7a866480a0819d4a862; 5bac54abce2a450a73bb38bbee8a04bba59ec7f4). - Static type checking improvements and seed-based markdown generation: improved type safety with mypy and generated markdown documentation from seed files (commits baa862b852daf0ae90fd7d05772abcd26a3de214; a71b5bfe3b23a38bfda5dac087411a9d4e0a747f). Major bugs fixed: - Ignore UV lock file during processing/build to prevent unintended changes (commit 70676678ba343c259d8c384a05f9fa079250d2e0). - Fix: S3Source model dump serialization bug (commit f078b14259f9dc1d1f7e4127986deaa6b1616b73). Other notable work and impact: - Shadows and Historic Resources chapter added, and new chapters for Natural Resources and Sewer data expanded the resource suite, strengthening coverage for planning scenarios. - Large features in GeoJSON support and updates to CHAS dataset improve data integrity and scalability of geospatial analyses. - Ingest configure test and additional template support (e.g., DEP stormwater templates) advance testing and data readiness. Overall impact and accomplishments: - Substantial expansion of data catalog and templates, enabling more complete planning datasets for analysis and reporting. - Improved pipeline reliability, traceability, and performance through versioned ingests, table materializations, and up-to-date projections. - Strengthened documentation and governance via seeds-based markdown generation and improved dataset naming. Technologies and skills demonstrated: - Data engineering: template management, data ingestion, modeling as tables, dataset provisioning. - Data quality and governance: mypy/type checking, seed-based docs generation, test scaffolding. - Geospatial and domain knowledge: MapPluto workflows, large GeoJSON features, CHAS, DEP stormwater, and natural resources chapters.
April 2025
28 Commits • 16 Features
Apr 1, 2025April 2025 monthly summary for NYCPlanning/data-engineering. The month delivered substantial enhancements to data templates, ingestion and modeling pipelines, and expanded dataset coverage, while tightening quality controls and documentation. The work is aligned with delivering reliable, scalable data products for planning analytics and downstream BI. Key features delivered: - MapPluto Template Updates and Clipped Workflow: refreshed dcp_mappluto_wi and dcp_mappluto_clipped templates and enabled clipped MapPluto workflow (commits e4326f519c7c064038834959bde03d03cc4d9063; d78b263c0bb3a8e9b305d4e6750db6bc7ed3d590; a9def3e42ea334ef29c2e01f729489475124723e). These changes improve map accuracy and performance for planning outputs. - Ingest and Modeling Enhancements: added versioning to ingest action run names, materialized all models as tables, and adopted the latest population projections file from the Housing team (commits 22e39d6ac2ef63587ba31744b42101a02eb9a520; 4e80dccc75a507f8f6889871c20fbc56de048258; 59eb62f5b4fdb57af0065d4b4e51d693d98c2093). - Ceqr Data Templates, Recipe, and Documentation: introduced Ceqr source data templates, updated the Ceqr recipe and readme, and adjusted dataset names for clarity (commits b7acc32dd3ced24da6ea8101cb5ad9a7d9d2dc05; 96497e78308548da516f8d197204e7c51b3c6236; c0dad3185894daacfdf6c699e0f85d8a88dedb43; c0d677276241b8baf8187ae6f8d8e72bd4ac55db). - Add New Datasets (DPR ParksProperties, DCP Lion, etc.): expanded data catalog with DPR ParksProperties, DCP Lion and additional datasets (commits 3e364fe7deb5f7d4ea4a6f1ed304d549336cd22d; 0b16064ec47da89d3abeb7a866480a0819d4a862; 5bac54abce2a450a73bb38bbee8a04bba59ec7f4). - Static type checking improvements and seed-based markdown generation: improved type safety with mypy and generated markdown documentation from seed files (commits baa862b852daf0ae90fd7d05772abcd26a3de214; a71b5bfe3b23a38bfda5dac087411a9d4e0a747f). Major bugs fixed: - Ignore UV lock file during processing/build to prevent unintended changes (commit 70676678ba343c259d8c384a05f9fa079250d2e0). - Fix: S3Source model dump serialization bug (commit f078b14259f9dc1d1f7e4127986deaa6b1616b73). Other notable work and impact: - Shadows and Historic Resources chapter added, and new chapters for Natural Resources and Sewer data expanded the resource suite, strengthening coverage for planning scenarios. - Large features in GeoJSON support and updates to CHAS dataset improve data integrity and scalability of geospatial analyses. - Ingest configure test and additional template support (e.g., DEP stormwater templates) advance testing and data readiness. Overall impact and accomplishments: - Substantial expansion of data catalog and templates, enabling more complete planning datasets for analysis and reporting. - Improved pipeline reliability, traceability, and performance through versioned ingests, table materializations, and up-to-date projections. - Strengthened documentation and governance via seeds-based markdown generation and improved dataset naming. Technologies and skills demonstrated: - Data engineering: template management, data ingestion, modeling as tables, dataset provisioning. - Data quality and governance: mypy/type checking, seed-based docs generation, test scaffolding. - Geospatial and domain knowledge: MapPluto workflows, large GeoJSON features, CHAS, DEP stormwater, and natural resources chapters.
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for NYCPlanning/data-engineering. Focused on reliability, schema alignment, and CI/CD enhancements for the CEQR data pipeline, delivering scalable export improvements, expanded data product options for promotions, and modernization of CI/CD and repository structure.
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for NYCPlanning/data-engineering. Focused on reliability, schema alignment, and CI/CD enhancements for the CEQR data pipeline, delivering scalable export improvements, expanded data product options for promotions, and modernization of CI/CD and repository structure.
March 2025
February 2025
14 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for NYCPlanning/data-engineering focused on delivering automated Excel-based CDBG reporting, standardizing data models/export schemas, and tightening CI workflow to improve visibility and reliability of data releases.
February 2025
14 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for NYCPlanning/data-engineering focused on delivering automated Excel-based CDBG reporting, standardizing data models/export schemas, and tightening CI workflow to improve visibility and reliability of data releases.
January 2025
42 Commits • 15 Features
Jan 1, 2025January 2025: Delivered a data-engineering refresh for NYCPlanning/data-engineering that enhances census data integration, data governance, and export capabilities, while tightening eligibility logic and improving documentation and CI/QA processes. The month included a major shift to new population data sources, standardized borough contracts, expanded DCP and CDBG data exports, and foundational geospatial/data-flow enhancements.
42 Commits • 15 Features
Jan 1, 2025January 2025: Delivered a data-engineering refresh for NYCPlanning/data-engineering that enhances census data integration, data governance, and export capabilities, while tightening eligibility logic and improving documentation and CI/QA processes. The month included a major shift to new population data sources, standardized borough contracts, expanded DCP and CDBG data exports, and foundational geospatial/data-flow enhancements.
January 2025
December 2024
12 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for NYCPlanning/data-engineering focusing on business value and technical achievements: Key features delivered - Metadata-Driven Data Dictionary Export (YAML, PDF, XLSX): generated from organization/product metadata with a dedicated YAML writer and PDF writer; tests updated. Commits include 5c9c8eaa8d850ec946da9c8dc5b7ccd2d645a34e, b566cf11dbdec64288dab54a6ed0f7cbd8bbfa2c, 7491901b3f7024acea3d8977e92322d1e7eab99f, 9ffda28f9f1c428792944dd8214f6a8f0c26a1a0, 31037e2ca37b3284195a3839cf96402f21df6976. - Data Dictionary Presentation and Styling Improvements: HTML presentation enhancements with new templates for attributes/columns, a dedicated CSS file, and improved layout with tag styling for readability (commit afa39d03188e222c0b6a14ccb6d2bcde9989fd1e). Major bugs fixed - Fixed missing environment variable in ingest action (commit 9946fb91c926ec436edc6c541c9e006028ecd9ac). - Dropped a failing dcpy test (commit 0169de39bac2c5334d53e531df5c8ddfaa4de879). - pdf_writer no longer returns file paths (commit 7491901b3f7024acea3d8977e92322d1e7eab99f). - Do not clone metadata repo during a build (commit 0db959105f2a11632d6a262d378ff98a720f2016). Overall impact and accomplishments - Significant automation improvements driving faster, more reliable data dictionary generation and distribution; reduced manual steps in doc generation; improved test reliability and build stability; better alignment between metadata and documentation. Technologies/skills demonstrated - Python tooling for YAML/PDF/XLSX generation, HTML/CSS templating, and data dictionary workflows; CI/CD improvements and GitHub connector usage; environment variable handling and test coverage enhancements.
December 2024
12 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for NYCPlanning/data-engineering focusing on business value and technical achievements: Key features delivered - Metadata-Driven Data Dictionary Export (YAML, PDF, XLSX): generated from organization/product metadata with a dedicated YAML writer and PDF writer; tests updated. Commits include 5c9c8eaa8d850ec946da9c8dc5b7ccd2d645a34e, b566cf11dbdec64288dab54a6ed0f7cbd8bbfa2c, 7491901b3f7024acea3d8977e92322d1e7eab99f, 9ffda28f9f1c428792944dd8214f6a8f0c26a1a0, 31037e2ca37b3284195a3839cf96402f21df6976. - Data Dictionary Presentation and Styling Improvements: HTML presentation enhancements with new templates for attributes/columns, a dedicated CSS file, and improved layout with tag styling for readability (commit afa39d03188e222c0b6a14ccb6d2bcde9989fd1e). Major bugs fixed - Fixed missing environment variable in ingest action (commit 9946fb91c926ec436edc6c541c9e006028ecd9ac). - Dropped a failing dcpy test (commit 0169de39bac2c5334d53e531df5c8ddfaa4de879). - pdf_writer no longer returns file paths (commit 7491901b3f7024acea3d8977e92322d1e7eab99f). - Do not clone metadata repo during a build (commit 0db959105f2a11632d6a262d378ff98a720f2016). Overall impact and accomplishments - Significant automation improvements driving faster, more reliable data dictionary generation and distribution; reduced manual steps in doc generation; improved test reliability and build stability; better alignment between metadata and documentation. Technologies/skills demonstrated - Python tooling for YAML/PDF/XLSX generation, HTML/CSS templating, and data dictionary workflows; CI/CD improvements and GitHub connector usage; environment variable handling and test coverage enhancements.
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024: NYCPlanning/data-engineering delivered key data pipeline reliability improvements and prepared groundwork for HTML-based reporting. Major accomplishments include fixing schema drift in the dca_operatingbusinesses pipeline, enhancing asset export reliability and observability, and laying the groundwork for HTML rendering and PDF workflows. These changes improve data correctness downstream, asset distribution accuracy, and developer velocity, supported by Python, SQL, and templating tooling with added tests and logging.
8 Commits • 2 Features
Nov 1, 2024November 2024: NYCPlanning/data-engineering delivered key data pipeline reliability improvements and prepared groundwork for HTML-based reporting. Major accomplishments include fixing schema drift in the dca_operatingbusinesses pipeline, enhancing asset export reliability and observability, and laying the groundwork for HTML rendering and PDF workflows. These changes improve data correctness downstream, asset distribution accuracy, and developer velocity, supported by Python, SQL, and templating tooling with added tests and logging.
November 2024
Activity
Loading activity data...
Quality Metrics
Correctness92.4%
Maintainability92.8%
Architecture90.6%
Performance87.8%
AI Usage20.2%
Skills & Technologies
Programming Languages
BashCSSCSVDockerfileHTMLJinjaMarkdownN/APythonSQL
Technical Skills
API IntegrationAWSAWS S3Asset GenerationBackend DevelopmentBug FixBuild AutomationBuild ScriptingCI/CDCSSCode OrganizationCode RefactoringCode RenamingConfiguration ManagementDBT
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline