NMDSdevopsServiceAdm/DataEngineering
Oct 2024 – Jul 2026
15 Months active
Languages Used
HCLPythonJSONSQLTOMLTerraformYAMLMarkdown
Technical Skills
AWS GlueAWS Step FunctionsCode CleanupCode FormattingData EngineeringData Processing
Marion Holloway
PROFILE
Marion Holloway
Over 15 months, contributed to NMDSdevopsServiceAdm/DataEngineering by architecting and maintaining robust data engineering pipelines focused on data quality, validation, and deployment reliability. Leveraging Python, PySpark, and Polars, developed modular ETL workflows, migrated core processing from Spark to Polars for scalability, and implemented automated CI/CD with Terraform and CircleCI. Enhanced data ingestion, schema management, and error handling, while expanding test coverage and validation frameworks to ensure safe, observable releases. Introduced memory-efficient processing, metadata validation, and infrastructure automation, resulting in resilient, maintainable pipelines. The work emphasized maintainability, test-driven development, and production-grade data governance across AWS-based cloud infrastructure.
Overall Statistics
Feature vs Bugs
74%Features
Repository Contributions
1,036Total
Bugs
107
Commits
1,036
Features
305
Lines of code
279,399
Activity Months15
Your Network
13 peopleSame Organization
@skillsforcare.org.uk3
Work History
July 2026
151 Commits • 50 Features
Jul 1, 2026July 2026 monthly summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering core data ingestion and processing capabilities, stabilizing test suites, and optimizing build and deployment workflows. Highlights include end-to-end entry point enhancements, data import improvements, and reliability improvements across batch validation and orchestration. The work contributes to more trustworthy data pipelines, faster feedback loops, and reproducible deployments.
151 Commits • 50 Features
Jul 1, 2026July 2026 monthly summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering core data ingestion and processing capabilities, stabilizing test suites, and optimizing build and deployment workflows. Highlights include end-to-end entry point enhancements, data import improvements, and reliability improvements across batch validation and orchestration. The work contributes to more trustworthy data pipelines, faster feedback loops, and reproducible deployments.
July 2026
June 2026
131 Commits • 43 Features
Jun 1, 2026June 2026 focused on stabilizing data configurations, expanding analytics capabilities, and boosting quality through testing and validation. Key outcomes include threshold persistence to S3 for durability, a new estimates schema with a job_group column to enable grouping logic, replacement of the old expression generator with a robust expression-based approach, improved argument handling for flexible runtime behavior, and expanded unit-test/validation coverage that includes expression generation tests, null handling, and piv_lf output checks. These changes reduce data drift, improve data lineage, and accelerate safe, observable releases across the data engineering pipeline.
June 2026
131 Commits • 43 Features
Jun 1, 2026June 2026 focused on stabilizing data configurations, expanding analytics capabilities, and boosting quality through testing and validation. Key outcomes include threshold persistence to S3 for durability, a new estimates schema with a job_group column to enable grouping logic, replacement of the old expression generator with a robust expression-based approach, improved argument handling for flexible runtime behavior, and expanded unit-test/validation coverage that includes expression generation tests, null handling, and piv_lf output checks. These changes reduce data drift, improve data lineage, and accelerate safe, observable releases across the data engineering pipeline.
May 2026
144 Commits • 39 Features
May 1, 2026May 2026 highlights for NMDSdevopsServiceAdm/DataEngineering: - Built foundational unit test scaffolding and testing utilities, enabling immediate test authoring and CI readiness (unit test setup, placeholder functions, and test data planning for 1574). - Advanced data processing with memory efficiency in mind: refactors to group, aggregate, explode, and memory-management strategies across dataframes; introduced column compression and memory partitioning approaches to reduce peak memory consumption. - Strengthened data validation, schema robustness, and datatype handling: established a metadata validation framework skeleton, improved Polars/schema typing, and implemented datatype casting and schema checks to prevent runtime errors in production pipelines. - Refactoring for clarity and maintainability: restructured filtering utilities and job role handling (enum-based job roles, mapping by job group), refactored expressions, and modularized tests for the Expressions class; updated test data and schemas across multiple scripts. - Quality and documentation: stabilized tests, cleaned up noisy test data (removal of comments, formatting fixes), updated changelog and docstrings, and prepared release notes for version changes; ongoing documentation alignment with batch changes. Overall, these efforts reduce production risk, accelerate delivery cycles, and improve confidence in data quality outputs while demonstrating strong proficiency in PySpark/Polars-based pipelines, test automation, and maintainable code practices.
144 Commits • 39 Features
May 1, 2026May 2026 highlights for NMDSdevopsServiceAdm/DataEngineering: - Built foundational unit test scaffolding and testing utilities, enabling immediate test authoring and CI readiness (unit test setup, placeholder functions, and test data planning for 1574). - Advanced data processing with memory efficiency in mind: refactors to group, aggregate, explode, and memory-management strategies across dataframes; introduced column compression and memory partitioning approaches to reduce peak memory consumption. - Strengthened data validation, schema robustness, and datatype handling: established a metadata validation framework skeleton, improved Polars/schema typing, and implemented datatype casting and schema checks to prevent runtime errors in production pipelines. - Refactoring for clarity and maintainability: restructured filtering utilities and job role handling (enum-based job roles, mapping by job group), refactored expressions, and modularized tests for the Expressions class; updated test data and schemas across multiple scripts. - Quality and documentation: stabilized tests, cleaned up noisy test data (removal of comments, formatting fixes), updated changelog and docstrings, and prepared release notes for version changes; ongoing documentation alignment with batch changes. Overall, these efforts reduce production risk, accelerate delivery cycles, and improve confidence in data quality outputs while demonstrating strong proficiency in PySpark/Polars-based pipelines, test automation, and maintainable code practices.
May 2026
April 2026
97 Commits • 18 Features
Apr 1, 2026April 2026 performance summary for NMDSdevopsServiceAdm/DataEngineering. The month focused on stabilizing the data pipeline, migrating core processing from Spark to Polars/LazyFrame for scalability, expanding test coverage and automation, and delivering data-quality improvements for imputation and care-home modeling. Business value was realized through faster, more reliable data processing, improved model diagnostics, and robust release hygiene.
April 2026
97 Commits • 18 Features
Apr 1, 2026April 2026 performance summary for NMDSdevopsServiceAdm/DataEngineering. The month focused on stabilizing the data pipeline, migrating core processing from Spark to Polars/LazyFrame for scalability, expanding test coverage and automation, and delivering data-quality improvements for imputation and care-home modeling. Business value was realized through faster, more reliable data processing, improved model diagnostics, and robust release hygiene.
March 2026
11 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Delivered targeted data engineering enhancements for NMDSdevopsServiceAdm/DataEngineering, focusing on CQC data filtering, location-based aggregation, and groundwork for location ID length filtering. Improvements include ingestion/schema alignment, updated changelog, and strengthened test coverage. The work enhances data quality, reporting accuracy, and maintainability for compliant workforce analytics.
11 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Delivered targeted data engineering enhancements for NMDSdevopsServiceAdm/DataEngineering, focusing on CQC data filtering, location-based aggregation, and groundwork for location ID length filtering. Improvements include ingestion/schema alignment, updated changelog, and strengthened test coverage. The work enhances data quality, reporting accuracy, and maintainability for compliant workforce analytics.
March 2026
February 2026
8 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering automated infrastructure workflows and policy hardening to accelerate reliable deployments and secure automation.
February 2026
8 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering automated infrastructure workflows and policy hardening to accelerate reliable deployments and secure automation.
January 2026
50 Commits • 13 Features
Jan 1, 2026Month: 2026-01 | NMDSdevopsServiceAdm/DataEngineering: Delivered reliability, data quality, and deployment readiness improvements. Ephemeral tag support was added to enable ephemeral labeling in commits and datasets, improving data lineage. Expanded test infrastructure and validation suite now includes row-count checks and utilities to catch data quality regressions early. Feature Columns Management and Row Count Enhancements harmonized feature column handling, updated dataset column naming, and strengthened row-count calculations with testing support. Environment and config updates align the workflow with the new process, including step function config, Docker dependencies, and CircleCI integration, reducing deployment friction and enabling safer releases. Robust maintenance work included code hygiene fixes (removing lingering TODOs and incorrect file commits), dataset variable corrections, and cleanup of deprecated references (e.g., DynamoDB references), plus documentation updates and unrecognised-model handling to improve production resilience. Technologies demonstrated: Python ETL tooling, test automation, Terraform and Terraform formatting, CircleCI configuration, Docker, data validation, and dataset schema management. Overall impact: shorter deployment cycles, fewer data-quality issues, clearer data lineage, and stronger defensibility against production regressions.
50 Commits • 13 Features
Jan 1, 2026Month: 2026-01 | NMDSdevopsServiceAdm/DataEngineering: Delivered reliability, data quality, and deployment readiness improvements. Ephemeral tag support was added to enable ephemeral labeling in commits and datasets, improving data lineage. Expanded test infrastructure and validation suite now includes row-count checks and utilities to catch data quality regressions early. Feature Columns Management and Row Count Enhancements harmonized feature column handling, updated dataset column naming, and strengthened row-count calculations with testing support. Environment and config updates align the workflow with the new process, including step function config, Docker dependencies, and CircleCI integration, reducing deployment friction and enabling safer releases. Robust maintenance work included code hygiene fixes (removing lingering TODOs and incorrect file commits), dataset variable corrections, and cleanup of deprecated references (e.g., DynamoDB references), plus documentation updates and unrecognised-model handling to improve production resilience. Technologies demonstrated: Python ETL tooling, test automation, Terraform and Terraform formatting, CircleCI configuration, Docker, data validation, and dataset schema management. Overall impact: shorter deployment cycles, fewer data-quality issues, clearer data lineage, and stronger defensibility against production regressions.
January 2026
December 2025
43 Commits • 9 Features
Dec 1, 2025December 2025 monthly summary for NMDSdevopsServiceAdm/DataEngineering: Key features delivered, critical fixes, and process improvements focused on data safety, reliability, and throughput. Major work included S3 bucket cleanup safeguards prior to branch destruction, batch size tuning to optimize processing throughput, and expanded testing coverage for deployment and location calculations. Additional progress encompassed archive data pipeline updates with Polars-based merge scaffolding, together with ongoing code quality improvements and documentation enhancements. Overall, these efforts reduced risk, improved data integrity, and accelerated deployment readiness.
December 2025
43 Commits • 9 Features
Dec 1, 2025December 2025 monthly summary for NMDSdevopsServiceAdm/DataEngineering: Key features delivered, critical fixes, and process improvements focused on data safety, reliability, and throughput. Major work included S3 bucket cleanup safeguards prior to branch destruction, batch size tuning to optimize processing throughput, and expanded testing coverage for deployment and location calculations. Additional progress encompassed archive data pipeline updates with Polars-based merge scaffolding, together with ongoing code quality improvements and documentation enhancements. Overall, these efforts reduced risk, improved data integrity, and accelerated deployment readiness.
November 2025
13 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — Delivered targeted data ingestion improvements for CQC data in NMDSdevopsServiceAdm/DataEngineering. Key features include selective column handling for CQC data ingestion, enabling import of only relevant columns for CQC locations and for data flattened from the CQC providers API, with column selection adjustments and related schema removal. Implemented date typing for CQC locations (registration_date and deregistration_date) to Date type to enhance validation and downstream processing. Integrated Polars schema definitions into the Docker image and removed usage of POLARS_LOCATION_SCHEMA from data processing. Stabilized CI/CD: temporarily disabled complex data type validation and adjusted CircleCI settings to improve reliability during ingestion and deployment. Code hygiene improvements included formatting changes. These changes collectively improve data quality, processing efficiency, and pipeline stability, enabling faster, more reliable ingestion of CQC data and enabling better business insights.
13 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — Delivered targeted data ingestion improvements for CQC data in NMDSdevopsServiceAdm/DataEngineering. Key features include selective column handling for CQC data ingestion, enabling import of only relevant columns for CQC locations and for data flattened from the CQC providers API, with column selection adjustments and related schema removal. Implemented date typing for CQC locations (registration_date and deregistration_date) to Date type to enhance validation and downstream processing. Integrated Polars schema definitions into the Docker image and removed usage of POLARS_LOCATION_SCHEMA from data processing. Stabilized CI/CD: temporarily disabled complex data type validation and adjusted CircleCI settings to improve reliability during ingestion and deployment. Code hygiene improvements included formatting changes. These changes collectively improve data quality, processing efficiency, and pipeline stability, enabling faster, more reliable ingestion of CQC data and enabling better business insights.
November 2025
October 2025
55 Commits • 14 Features
Oct 1, 2025October 2025 performance summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering business value through data quality improvements, configurability, and reliable CI/CD readiness. Achievements include implementing a Postcode Corrections dictionary with CSV-based loading (and URI support) plus tests; enhancing argument configurability across Glue, Terraform, and job args; extending postcode-related configuration with an argument to the create_postcode_dim function; expanding test coverage and test data management; and improving documentation, code quality, and packaging to support maintainability and deployment resilience.
October 2025
55 Commits • 14 Features
Oct 1, 2025October 2025 performance summary for NMDSdevopsServiceAdm/DataEngineering focusing on delivering business value through data quality improvements, configurability, and reliable CI/CD readiness. Achievements include implementing a Postcode Corrections dictionary with CSV-based loading (and URI support) plus tests; enhancing argument configurability across Glue, Terraform, and job args; extending postcode-related configuration with an argument to the create_postcode_dim function; expanding test coverage and test data management; and improving documentation, code quality, and packaging to support maintainability and deployment resilience.
February 2025
20 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for NMDSdevopsServiceAdm/DataEngineering: Delivered key data quality and deployment reliability improvements through postcode dictionary enhancements, CI/CD alignment to main, and comprehensive CQC location data cleaning with imputation and schema updates. These changes tightened data integrity, reduced lookup errors, and stabilized release processes for primary development work.
20 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for NMDSdevopsServiceAdm/DataEngineering: Delivered key data quality and deployment reliability improvements through postcode dictionary enhancements, CI/CD alignment to main, and comprehensive CQC location data cleaning with imputation and schema updates. These changes tightened data integrity, reduced lookup errors, and stabilized release processes for primary development work.
February 2025
January 2025
73 Commits • 33 Features
Jan 1, 2025Month: 2025-01 — NMDSdevopsServiceAdm/DataEngineering. Focused on increasing maintainability, data quality, and delivery velocity for LM engagement features. Delivered modular code, validated data pipelines, and updated schemas while strengthening test coverage and aligning infra. Key deliverables span code refactors, data validation/schema enhancements, test modernization, and targeted deployment/infra improvements, all aimed at reducing risk and accelerating future iterations for data engineering workflows.
January 2025
73 Commits • 33 Features
Jan 1, 2025Month: 2025-01 — NMDSdevopsServiceAdm/DataEngineering. Focused on increasing maintainability, data quality, and delivery velocity for LM engagement features. Delivered modular code, validated data pipelines, and updated schemas while strengthening test coverage and aligning infra. Key deliverables span code refactors, data validation/schema enhancements, test modernization, and targeted deployment/infra improvements, all aimed at reducing risk and accelerating future iterations for data engineering workflows.
December 2024
65 Commits • 27 Features
Dec 1, 2024December 2024 monthly summary for NMDSdevopsServiceAdm/DataEngineering. Focused on hardening the data ingestion and processing pipelines, strengthening data governance, and expanding infrastructure automation. Delivered end-to-end validation, robust error handling, and schema consistency across components, enabling safer deployments and faster issue resolution. Demonstrated strong emphasis on reliability, data quality, and repeatable operations through IaC and comprehensive tests.
65 Commits • 27 Features
Dec 1, 2024December 2024 monthly summary for NMDSdevopsServiceAdm/DataEngineering. Focused on hardening the data ingestion and processing pipelines, strengthening data governance, and expanding infrastructure automation. Delivered end-to-end validation, robust error handling, and schema consistency across components, enabling safer deployments and faster issue resolution. Demonstrated strong emphasis on reliability, data quality, and repeatable operations through IaC and comprehensive tests.
December 2024
November 2024
146 Commits • 41 Features
Nov 1, 2024In November 2024, NMDSdevopsServiceAdm/DataEngineering delivered a reliable data orchestration and batch-processing foundation, strengthened testing stability, and improved data quality and observability. Key work spanned Step Function reliability enhancements, batch workflow infrastructure, archiving plan improvements, data dataset updates, and code quality/documentation improvements that reduce maintenance cost and accelerate future delivery.
November 2024
146 Commits • 41 Features
Nov 1, 2024In November 2024, NMDSdevopsServiceAdm/DataEngineering delivered a reliable data orchestration and batch-processing foundation, strengthened testing stability, and improved data quality and observability. Key work spanned Step Function reliability enhancements, batch workflow infrastructure, archiving plan improvements, data dataset updates, and code quality/documentation improvements that reduce maintenance cost and accelerate future delivery.
October 2024
29 Commits • 8 Features
Oct 1, 2024October 2024 – NMDSdevopsServiceAdm/DataEngineering: Implemented unit test scaffolding for the rolling average function, stabilized tests, and shipped CI and infrastructure scaffolding to support batch processing. Completed data schema/metrics enhancements, archive/dataset management updates, and robust code quality improvements. Resolved key stability bugs and improved maintainability, test reliability, and business value of data pipelines.
29 Commits • 8 Features
Oct 1, 2024October 2024 – NMDSdevopsServiceAdm/DataEngineering: Implemented unit test scaffolding for the rolling average function, stabilized tests, and shipped CI and infrastructure scaffolding to support batch processing. Completed data schema/metrics enhancements, archive/dataset management updates, and robust code quality improvements. Resolved key stability bugs and improved maintainability, test reliability, and business value of data pipelines.
October 2024
Activity
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Quality Metrics
Correctness90.6%
Maintainability89.8%
Architecture85.4%
Performance84.8%
AI Usage30.6%
Skills & Technologies
Programming Languages
CSVDockerfileHCLJSONMarkdownPythonSQLShellTOMLTerraform
Technical Skills
AI integrationAPI IntegrationAPI Integration TestingAPI integrationAWSAWS CLIAWS ECRAWS FargateAWS GlueAWS IAMAWS LambdaAWS S3AWS Step FunctionsApache ParquetApache Spark
Repositories Contributed To
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