Linux)

Science Systems & Applications•Lanham, MD

1d•$95,000 - $115,000•Onsite

About The Position

Science Systems and Applications, Inc. (SSAI) is seeking a Scientific Data Software Engineer to support the automated ingestion, validation, cataloging, and archiving of pipelines that power this mission-critical data center. In this role, you will develop and maintain robust Python software, shell scripts, and system-level automations running on Linux servers handling continuous, high-volume streams of scientific data. You will work closely with domain scientists, systems administrators, and data managers to ensure geodetic datasets are processed accurately, metadata is reliably extracted, and all files meet stringent archive integrity standards for global scientific distribution.

Requirements

Bachelor’s (B.S.) degree in Computer Science, Software Engineering, Earth/Geophysical Sciences, GIS, or a related technical discipline.
2+ years of professional or research software engineering experience using Python for automated data pipelines, file I/O operations, text parsing, and system interaction (os, sys, pathlib, subprocess,pandas).
Strong everyday comfort in a Linux command-line environment, including file permission management, process monitoring, and shell scripting (Bash).
Demonstrated experience managing codebase repositories, branching, and pull requests via Git (GitHub or GitLab).
Proven ability to debug data anomalies and trace runtime errors.
U.S. Citizenship required.

Nice To Haves

Familiarity with space geodesy or Earth science file formats (e.g., RINEX, SINEX, HDF5, or NetCDF).
Experience with relational databases and writing queries in SQL (PostgreSQL, MySQL, or SQLite).
Exposure to RESTful APIs, AWS/cloud object storage (S3), or containerization tools (Docker).
Familiarity with NASA ESDIS standards, Open Geospatial Consortium (OGC) protocols, or scientific metadata schemas.
Experience with other programming languages such as Java, C, etc.

Responsibilities

Design, implement, test, and maintain modular Python applications to automate end-to-end data ingestion, metadata parsing, format standardization, and archival storage workflows.
Author automated validation routines and sanity checks to catch data corruption, file truncation, and format anomalies prior to long-term archiving.
Triage runtime pipeline failures, diagnose I/O bottlenecks, and resolve operational issues impacting data availability.
Interface with geodesy scientists, archive engineers, and external data providers to align pipeline capabilities with community standards and data specifications.