Software Engineer

Spatial Front•College Park, MD

7d•Hybrid

About The Position

We are seeking a Software Engineer to help modernize, maintain, and enhance critical visualization and data processing applications for NOAA weather models and dissemination pipelines. In this role, you will apply modern software engineering practices—clean code, automated testing, cloud integrations, and agile workflows—to deliver reliable scientific imaging systems. While a background in meteorology or scientific data is preferred, strong software engineering capabilities with a willingness to learn atmospheric data concepts are key.

Requirements

Bachelor’s degree in Meteorology, Atmospheric Science, Physics, Computer Science, or a related field— OR ~2+ years of experience working with meteorological data/systems.
AWS Certified AI Practitioner or experience leveraging cloud-native AI tools.
3+ years of software engineering experience focusing on production Python and Linux/Unix environments (Bash/shell scripting).
Proficiency with standard version control systems (Git), CI/CD practices, and Agile tools (e.g., Jira, Confluence).
Fundamental knowledge of cloud platforms (AWS, Azure, or GCP) with a basic cloud certification (e.g., AWS Certified Cloud Practitioner, Azure Fundamentals, or GCP Digital Leader).
Excellent verbal and written technical communication skills for status updates and cross-team collaboration.
US Citizen and/or Current Green Card Holder (for clearance requirements).
Candidates will be subject to a complete background check.

Nice To Haves

Hands-on experience with scientific data formats (NetCDF, HDF5, GRIB/GRIB2).
Familiarity with meteorological visualization tools or packages (e.g., MetPy, Matplotlib, GEMPAK, GrADS, or NCL).
Understanding of scientific data dissemination protocols (e.g., LDM).
Reading/basic understanding of Fortran, Java, or PHP for refactoring/migration efforts.

Responsibilities

Design, build, test, and maintain robust Python-based services and scripting pipelines used for imaging weather model output and supporting dissemination systems.
Refactor legacy codebase, improve performance through benchmarking, and perform code reviews to maintain high standards of code readability and maintainability.
Participate actively in an Agile development environment—engaging in sprint planning, code reviews, and cross-functional alignment with product owners, designers, and project leads.
Maintain clear technical documentation, release notes, and deployment plans to support seamless delivery and knowledge sharing across the team.
Leverage modern AI tools to accelerate development, refactoring, and code analysis.