SCI clearance(or SCI-eligibility)

Oteemo, Inc•Reston, VA

6d•Remote

About The Position

Join Oteemo and become part of a transformation powerhouse where innovation meets impact. We're not just another consulting firm—we're architects of digital evolution, blending cutting-edge technical expertise with human-centered design principles to create solutions that resonate. Our work spans Infrastructure, Software Development, DevSecOps, Cybersecurity, Experience and Design, Organizational Change Management, and AI-enabled solutions, but our approach is what truly sets us apart. We measure success through tangible business outcomes, not billable hours. We foster a culture of continuous learning where your ideas can thrive and technical excellence is celebrated. Our collaborative global team works across borders and time zones, tackling complex challenges for both Commercial Enterprise and Federal Defense clients with equal passion and precision. At Oteemo, you'll have the opportunity to work with emerging technologies and develop your skills alongside industry experts who are reshaping digital landscapes. If you're seeking a place where your technical prowess can drive meaningful change and where innovation isn't just encouraged—it's expected—Oteemo is your next career destination.

Requirements

Active TS/SCI clearance (or SCI-eligibility), ideally with past or current DoD SAP/SAR access.
Advanced degree in a quantitative field (e.g., computer science, machine learning, applied statistics, or mathematics) or equivalent experience, with 7–8 years of relevant experience.
Proven experience with graph databases and analytics, including Neo4j, Gremlin, or similar tools, and query languages like Cypher or Gremlin; ability to model complex system relationships, workflows, and time-dependent processes.
Strong programming skills in modern languages such as Python, Java, Node.js, or Go, with expertise in writing clean, maintainable, and scalable code.
Experience building and integrating web application back ends and contributing to front-end development when needed.
Extensive experience with data engineering and pipelines, including ETL, data quality, and working across structured, semi-structured, and unstructured data; familiarity with event streaming, real-time data processing, and high-velocity sequential data flows.
Practical knowledge of software engineering best practices, including DevOps, DataOps, MLOps, containerization (e.g., Docker), and orchestration.
Experience with distributed computing frameworks and cloud platforms, with a focus on deploying enterprise applications in cloud environments.
Strong testing skills, including unit testing, integration/API testing, and ensuring robust, scalable solutions.
Experience with NoSQL databases and working with graph-related problems, including the use of GenAI/ML techniques like GraphRAG.
Proven ability to align data engineering approaches with large-scale interconnected systems, ensuring adaptability and scalability.

Nice To Haves

Past or current DoD Special Access Program (SAP) / Special Access Required (SAR) access.
Familiarity with aerospace and defense programs and/or mission data.
Hands-on experience with FastAPI, Pandas, and React + TypeScript.
Interest or experience in running simulations in Python and applying advanced analytics to solve complex problems.

Responsibilities

Design, model, and implement graph data structures that capture complex system relationships, workflows, and time-dependent processes using Neo4j, Gremlin, or similar tools.
Build and integrate web application back ends and contribute to front-end development as needed to deliver complete, production-ready features.
Develop and maintain robust ETL and data pipelines spanning structured, semi-structured, and unstructured data, with strong attention to data quality.
Engineer real-time and event-streaming workflows that handle high-velocity, sequential data flows.
Deploy and operate enterprise applications in cloud environments using containerization and orchestration, applying DevOps, DataOps, and MLOps practices.
Apply GenAI/ML techniques, including GraphRAG, to graph-related and NoSQL data problems.
Write thorough unit, integration, and API tests to ensure robust, scalable, and maintainable solutions.
Run simulations and apply advanced analytics in Python to solve complex mission problems.
Align data engineering approaches with large-scale interconnected systems to ensure adaptability and scalability.