> Markdown version of [/jobs/ext/4260288-mid-level-python-software-engineer-305k-yr-ts-sci-fs-poly](https://www.wearedevelopers.com/jobs/ext/4260288-mid-level-python-software-engineer-305k-yr-ts-sci-fs-poly). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Mid-Level Python Software Engineer [$305k/yr+] TS/SCI-FS Poly - **Company:** SYSTOLIC, INC. - **Location:** Herndon, VA, United States - **Experience:** Experienced - **Salary:** $305,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Amazon Web Services, Microsoft Azure, C Sharp (Programming Language), DevOps, Elasticsearch, Data Flow Control, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, MySQL, NoSQL, Operational Data Store, Scrum Methodology, Containerization, Docker, Vulnerability Analysis, Golang - **Published:** October 31, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9275635/mid-level-python-software-engineer-305kyr-tssci-fs-poly ## About the Role * Degree: Technical bachelor's degree or equivalent experience * Years of experience: 8+ years * Total Compensation: $305k+ yearly (tentative) ## Description * Develop and maintain advanced data exploitation platforms supporting critical data collection processes. * Ingest, transform, model, and manage structured, semi-structured, and unstructured datasets within modern cloud environments., * Design, construct, and maintain scalable data exploitation pipelines and dataflow platforms using Python, Java, Go, C#, or Scala. * Ingest and store diverse datasets leveraging relational and NoSQL databases, including PostgreSQL, MySQL, MongoDB, and Elasticsearch. * Deploy, containerize, and orchestrate services using Docker and Kubernetes across AWS and Microsoft Azure cloud infrastructures. * Participate in Agile/Scrum development cycles, perform requirements analysis, and support system vulnerability assessments and DevOps best practices. * Support the integration of machine learning and artificial intelligence capabilities into operational data pipelines.