> Markdown version of [/jobs/ext/4657766-mid-level-java-software-engineer-346k-yr-ts-sci-fs-poly](https://www.wearedevelopers.com/jobs/ext/4657766-mid-level-java-software-engineer-346k-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 Java Software Engineer [$346k/yr+] TS/SCI-FS Poly - **Company:** SYSTOLIC, INC. - **Location:** Annapolis Junction, MD, United States - **Experience:** Experienced - **Salary:** $346,000 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Data Analysis, Confluence, JIRA, Big Data, Information Engineering, Extract Transform Load (ETL), Data Systems, Elasticsearch, Apache Hadoop, Python (Programming Language), Reliability Engineering, Software Engineering, Git, Kubernetes, Apache Nifi, Kibana, Docker - **Published:** November 10, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9300963/mid-level-java-software-engineer-346kyr-tssci-fs-poly ## About the Role * Degree: Technical bachelor's degree or equivalent experience * Years of experience: 12+ years * Total Compensation: $346k+ yearly ## Description * Lead a technical team operating an enterprise business intelligence platform supporting data engineering, analytics, and automation initiatives. * Design and implement robust ETL workflows to process, normalize, and enrich complex large-scale datasets. * Develop software solutions primarily in Java and Python while containerizing services using Docker and Kubernetes. * Collaborate with stakeholders to translate mission needs into technical requirements and reliable operational capabilities., * Serve as technical team lead, breaking down tasks, managing expectations, and mentoring junior engineers. * Develop analytics and ETL pipelines to normalize and enrich large data sets for data-driven decision-making. * Design, develop, and deploy software services utilizing Java, Python, Docker, and Kubernetes. * Augment platforms with modern data technologies, including Elasticsearch, Kibana, Apache NiFi, and Hadoop. * Implement Site Reliability Engineering (SRE) practices to ensure platform availability, reliability, and quality. * Collaborate across diverse engineering disciplines and manage task tracking using Git, Jira, and Confluence.