
Maxine Hartnett developed and maintained core backend features for the IMAP-Science-Operations-Center/sds-data-manager repository, focusing on data pipeline reliability, configuration management, and cloud infrastructure. She engineered robust AWS Lambda and SQS-based workflows to improve batch job monitoring, event routing, and delayed queue handling, while enhancing data integrity through dependency configuration and input validation. Using Python, AWS CDK, and SQLAlchemy, Maxine expanded test coverage, modernized DevOps tooling, and streamlined deployment processes. Her work addressed security, maintainability, and data governance, enabling reproducible builds and stable data ingestion. The depth of her contributions reflects strong ownership of complex, production-grade data systems.

In 2025-10, focused on stabilizing the MAG data processing pipeline by integrating SCLK dependencies (SPICE), updating configurations, improving test coverage for L1C SPICE paths, and performing routine release engineering to bump sds-data-manager to 9.0.1. These efforts reduce data-processing risk, accelerate release readiness, and improve overall maintainability.
In 2025-10, focused on stabilizing the MAG data processing pipeline by integrating SCLK dependencies (SPICE), updating configurations, improving test coverage for L1C SPICE paths, and performing routine release engineering to bump sds-data-manager to 9.0.1. These efforts reduce data-processing risk, accelerate release readiness, and improve overall maintainability.
2025-09 monthly summary for IMAP-Science-Operations-Center/sds-data-manager. Delivered two key features to strengthen batch execution and pipeline configurability; stabilized test coverage to reduce regressions; and enabled support for new data types and kernels across multiple pipelines.
2025-09 monthly summary for IMAP-Science-Operations-Center/sds-data-manager. Delivered two key features to strengthen batch execution and pipeline configurability; stabilized test coverage to reduce regressions; and enabled support for new data types and kernels across multiple pipelines.
August 2025 performance summary for IMAP-Science-Operations-Center/sds-data-manager. Delivered end-to-end improvements to the L1D data processing pipeline and job submission workflow, with a focus on data integrity, reliability, and operational flexibility. Key outcomes include hard dependency configuration enhancements, error-prone config duplicates resolved, and improved date handling for scheduling via string-based inputs and optional repointing. CI and tests were updated to reflect parsing and dependency hash formatting changes, ensuring stable deployments and reproducible results across environments.
August 2025 performance summary for IMAP-Science-Operations-Center/sds-data-manager. Delivered end-to-end improvements to the L1D data processing pipeline and job submission workflow, with a focus on data integrity, reliability, and operational flexibility. Key outcomes include hard dependency configuration enhancements, error-prone config duplicates resolved, and improved date handling for scheduling via string-based inputs and optional repointing. CI and tests were updated to reflect parsing and dependency hash formatting changes, ensuring stable deployments and reproducible results across environments.
July 2025 monthly performance summary for IMAP-Science-Operations-Center/sds-data-manager. Focused on stabilizing data routing and SPICE MAG data dependencies. Key work delivered included an SQS delayed event routing accuracy improvement and targeted MAG dependency configuration adjustments for SPICE, resulting in more reliable data delivery and processing across the pipeline. These changes reduce misrouting risks, improve data quality, and enable downstream analytics and instrument operations.
July 2025 monthly performance summary for IMAP-Science-Operations-Center/sds-data-manager. Focused on stabilizing data routing and SPICE MAG data dependencies. Key work delivered included an SQS delayed event routing accuracy improvement and targeted MAG dependency configuration adjustments for SPICE, resulting in more reliable data delivery and processing across the pipeline. These changes reduce misrouting risks, improve data quality, and enable downstream analytics and instrument operations.
June 2025 monthly summary for IMAP-Science-Operations-Center/sds-data-manager. Delivered a coherent set of features that strengthen data processing configuration, pipeline determinism, and data governance, while expanding test coverage and hardening the system against config drift. Key outcomes include improved dependency configuration management for l2 processing levels (leapseconds and spacecraft_clock) with SPICE-related test coverage, enabling more reliable data fusion timing; a Glows dataset dependency configuration to ensure L1B→L2 progression with HARD triggers and downstream dependencies; expanded S3 object tagging to support metadata tagging across data buckets via GetObjectTagging and PutObjectTagging with updated IAM policies; and BatchStarter robustness improvements introducing delayed SQS queues, multi-queue handling, updated queue URL logic, and broader test coverage for multi-queue scenarios. These changes collectively reduce configuration drift, increase pipeline determinism, and enhance data governance. Technologies/skills demonstrated include AWS IAM/S3 tagging, SQS-based orchestration, SPICE/test coverage integration, configuration management for data processing pipelines, and targeted test expansion to validate multi-queue scenarios.
June 2025 monthly summary for IMAP-Science-Operations-Center/sds-data-manager. Delivered a coherent set of features that strengthen data processing configuration, pipeline determinism, and data governance, while expanding test coverage and hardening the system against config drift. Key outcomes include improved dependency configuration management for l2 processing levels (leapseconds and spacecraft_clock) with SPICE-related test coverage, enabling more reliable data fusion timing; a Glows dataset dependency configuration to ensure L1B→L2 progression with HARD triggers and downstream dependencies; expanded S3 object tagging to support metadata tagging across data buckets via GetObjectTagging and PutObjectTagging with updated IAM policies; and BatchStarter robustness improvements introducing delayed SQS queues, multi-queue handling, updated queue URL logic, and broader test coverage for multi-queue scenarios. These changes collectively reduce configuration drift, increase pipeline determinism, and enhance data governance. Technologies/skills demonstrated include AWS IAM/S3 tagging, SQS-based orchestration, SPICE/test coverage integration, configuration management for data processing pipelines, and targeted test expansion to validate multi-queue scenarios.
May 2025 performance summary for IMAP-Science-Operations-Center/sds-data-manager: Delivered end-to-end CR data support in ScienceFiles and ingestion/indexing, updated core dependencies to improve stability and compatibility, and refined MAG data processing relationships to support downstream analytics. These changes enable richer CR metrics ingestion and querying, improve build reliability, and strengthen data product mappings for MAG workflows.
May 2025 performance summary for IMAP-Science-Operations-Center/sds-data-manager: Delivered end-to-end CR data support in ScienceFiles and ingestion/indexing, updated core dependencies to improve stability and compatibility, and refined MAG data processing relationships to support downstream analytics. These changes enable richer CR metrics ingestion and querying, improve build reliability, and strengthen data product mappings for MAG workflows.
April 2025 monthly summary for IMAP-Science-Operations-Center/sds-data-manager. Focused on stabilizing batch processing workflows and modernizing DevOps tooling to improve reliability, test coverage, and CI/CD reproducibility. Key engineering work delivered a robustness fix in Batch Starter Lambda, plus comprehensive tooling updates to dependencies and development workflows. The changes reduce batch failures, improve observability and logs, and shorten onboarding time for new contributors.
April 2025 monthly summary for IMAP-Science-Operations-Center/sds-data-manager. Focused on stabilizing batch processing workflows and modernizing DevOps tooling to improve reliability, test coverage, and CI/CD reproducibility. Key engineering work delivered a robustness fix in Batch Starter Lambda, plus comprehensive tooling updates to dependencies and development workflows. The changes reduce batch failures, improve observability and logs, and shorten onboarding time for new contributors.
March 2025 monthly summary focusing on security hardening and dependency hygiene for SDS Data Manager. Primary work: Lambda Layer dependency updates to latest Python 3.9+ compatible releases (certifi, charset-normalizer, and related libraries), improving security posture and compatibility. No new user-facing features released this month; emphasis on reliability, security, and maintainability.
March 2025 monthly summary focusing on security hardening and dependency hygiene for SDS Data Manager. Primary work: Lambda Layer dependency updates to latest Python 3.9+ compatible releases (certifi, charset-normalizer, and related libraries), improving security posture and compatibility. No new user-facing features released this month; emphasis on reliability, security, and maintainability.
December 2024 monthly summary for IMAP-Science-Operations-Center/sds-data-manager focusing on delivering a robust monitoring solution for AWS Batch failures and improving reliability and incident response across the data processing pipeline.
December 2024 monthly summary for IMAP-Science-Operations-Center/sds-data-manager focusing on delivering a robust monitoring solution for AWS Batch failures and improving reliability and incident response across the data processing pipeline.
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